{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\user\Box\COVID-19 IPE\Analysis\FPA\Dataverse\fpa_replication_log.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}19 Jul 2022, 11:13:45

{com}. do "C:\Users\user\Box\COVID-19 IPE\Analysis\FPA\Dataverse\fpa_replication.do"
{txt}
{com}. ***************************
. ***** MAIN MANUSCRIPT *****
. ***************************
. 
. use "COVID19_TRAC.dta", clear
{txt}
{com}. 
. * ssc install estout, replace
. 
. * Table 1: Category 1 Entry Restrictions * 
. 
. stset date, id (cowcode) failure (be_cat_1_China_carryover)

                {txt}id:  {res}cowcode
     {txt}failure event:  {res}be_cat_1_China_carryover != 0 & be_cat_1_China_carryover < .
{txt}obs. time interval:  {res}(date[_n-1], date]
{txt} exit on or before:  {res}failure

{txt}{hline 78}
{res}     14,235{txt}  total observations
{res}      2,774{txt}  observations begin on or after (first) failure
{hline 78}
{res}     11,461{txt}  observations remaining, representing
{res}        195{txt}  subjects
{res}         76{txt}  failures in single-failure-per-subject data
{res}  4,287,421{txt}  total analysis time at risk and under observation
                                                at risk from t = {res}        0
                                     {txt}earliest observed entry t = {res}        0
                                          {txt}last observed exit t = {res}   22,001
{txt}
{com}. stsum

         {txt}failure _d:  {res}be_cat_1_China_carryover
   {txt}analysis time _t:  {res}date
                 {txt}id:  {res}cowcode

{txt}{col 10}{c |}{col 26}incidence{col 42}no. of{col 52}{c LT}{hline 6} Survival time {hline 5}{c RT}
{col 10}{c |} time at risk{col 29}rate{col 41}subjects{col 57}25%{col 67}50%{col 77}75%
{hline 9}{c +}{hline 69}
   total {c |} {res}     4287421   .0000177{col 40}      195      21974         .         .
{txt}
{com}. 
. eststo clear 
{txt}
{com}. eststo: stcox contract_workers_abroad_2018_pc migration_from_china_pc2019 migration_total2019 log_gdp log_gdp_pc log_resource_pc polity2 health_2018 distcap_china deaths_7_day_pc, nohr vce(robust)

         {txt}failure _d:  {res}be_cat_1_China_carryover
   {txt}analysis time _t:  {res}date
                 {txt}id:  {res}cowcode

{txt}Iteration 0:   log pseudolikelihood = {res}-268.22726
{txt}Iteration 1:   log pseudolikelihood = {res}-257.43228
{txt}Iteration 2:   log pseudolikelihood = {res}-253.73165
{txt}Iteration 3:   log pseudolikelihood = {res}-253.27215
{txt}Iteration 4:   log pseudolikelihood = {res}-252.57701
{txt}Iteration 5:   log pseudolikelihood = {res}-251.92677
{txt}Iteration 6:   log pseudolikelihood = {res}-251.70565
{txt}Iteration 7:   log pseudolikelihood = {res}-251.68248
{txt}Iteration 8:   log pseudolikelihood = {res}-251.68217
{txt}Iteration 9:   log pseudolikelihood = {res}-251.68217
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res}-251.68217

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         148             {txt}Number of obs    =  {res}     8,951
{txt}No. of failures      = {res}          56
{txt}Time at risk         = {res}     3254295
                                                {txt}Wald chi2({res}10{txt})    =  {res}     68.32
{txt}Log pseudolikelihood =   {res}-251.68217             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 97:(Std. Err. adjusted for {res:148} clusters in cowcode)}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                             _t{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      z{col 65}   P>|z|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
contract_workers_abroad_2018_pc {c |}{col 33}{res}{space 2} .8154522{col 45}{space 2} .2142178{col 56}{space 1}    3.81{col 65}{space 3}0.000{col 73}{space 4}  .395593{col 86}{space 3} 1.235311
{txt}{space 4}migration_from_china_pc2019 {c |}{col 33}{res}{space 2} .1021903{col 45}{space 2} .0219546{col 56}{space 1}    4.65{col 65}{space 3}0.000{col 73}{space 4} .0591599{col 86}{space 3} .1452206
{txt}{space 12}migration_total2019 {c |}{col 33}{res}{space 2}-.0055275{col 45}{space 2} .0103875{col 56}{space 1}   -0.53{col 65}{space 3}0.595{col 73}{space 4}-.0258866{col 86}{space 3} .0148316
{txt}{space 24}log_gdp {c |}{col 33}{res}{space 2}-.0912972{col 45}{space 2} .1055803{col 56}{space 1}   -0.86{col 65}{space 3}0.387{col 73}{space 4}-.2982307{col 86}{space 3} .1156364
{txt}{space 21}log_gdp_pc {c |}{col 33}{res}{space 2}-.0081379{col 45}{space 2} .1721835{col 56}{space 1}   -0.05{col 65}{space 3}0.962{col 73}{space 4}-.3456114{col 86}{space 3} .3293355
{txt}{space 16}log_resource_pc {c |}{col 33}{res}{space 2} .1346247{col 45}{space 2} .0762584{col 56}{space 1}    1.77{col 65}{space 3}0.078{col 73}{space 4}-.0148391{col 86}{space 3} .2840885
{txt}{space 24}polity2 {c |}{col 33}{res}{space 2} .0093652{col 45}{space 2}  .029943{col 56}{space 1}    0.31{col 65}{space 3}0.754{col 73}{space 4}-.0493219{col 86}{space 3} .0680523
{txt}{space 20}health_2018 {c |}{col 33}{res}{space 2}-.0242004{col 45}{space 2} .0828806{col 56}{space 1}   -0.29{col 65}{space 3}0.770{col 73}{space 4}-.1866435{col 86}{space 3} .1382427
{txt}{space 18}distcap_china {c |}{col 33}{res}{space 2}-.1019433{col 45}{space 2} .0469798{col 56}{space 1}   -2.17{col 65}{space 3}0.030{col 73}{space 4}-.1940221{col 86}{space 3}-.0098646
{txt}{space 16}deaths_7_day_pc {c |}{col 33}{res}{space 2}-27.01866{col 45}{space 2} 17.24907{col 56}{space 1}   -1.57{col 65}{space 3}0.117{col 73}{space 4}-60.82621{col 86}{space 3} 6.788893
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}. eststo: stcox labor_services_workers_2018_pc migration_from_china_pc2019 migration_total2019 log_gdp log_gdp_pc log_resource_pc polity2 health_2018 distcap_china deaths_7_day_pc, nohr vce(robust)

         {txt}failure _d:  {res}be_cat_1_China_carryover
   {txt}analysis time _t:  {res}date
                 {txt}id:  {res}cowcode

{txt}Iteration 0:   log pseudolikelihood = {res}-268.22726
{txt}Iteration 1:   log pseudolikelihood = {res}-267.45135
{txt}Iteration 2:   log pseudolikelihood = {res}-260.22089
{txt}Iteration 3:   log pseudolikelihood = {res} -256.3868
{txt}Iteration 4:   log pseudolikelihood = {res}-254.91325
{txt}Iteration 5:   log pseudolikelihood = {res}-254.20992
{txt}Iteration 6:   log pseudolikelihood = {res}-253.79521
{txt}Iteration 7:   log pseudolikelihood = {res} -253.7042
{txt}Iteration 8:   log pseudolikelihood = {res}-253.69949
{txt}Iteration 9:   log pseudolikelihood = {res}-253.69948
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res}-253.69948

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         148             {txt}Number of obs    =  {res}     8,951
{txt}No. of failures      = {res}          56
{txt}Time at risk         = {res}     3254295
                                                {txt}Wald chi2({res}10{txt})    =  {res}     37.32
{txt}Log pseudolikelihood =   {res}-253.69948             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 96:(Std. Err. adjusted for {res:148} clusters in cowcode)}
{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}    Robust
{col 1}                            _t{col 32}{c |}      Coef.{col 44}   Std. Err.{col 56}      z{col 64}   P>|z|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
labor_services_workers_2018_pc {c |}{col 32}{res}{space 2} .1213222{col 44}{space 2} .1214389{col 55}{space 1}    1.00{col 64}{space 3}0.318{col 72}{space 4}-.1166936{col 85}{space 3}  .359338
{txt}{space 3}migration_from_china_pc2019 {c |}{col 32}{res}{space 2} .0757319{col 44}{space 2} .0381768{col 55}{space 1}    1.98{col 64}{space 3}0.047{col 72}{space 4} .0009068{col 85}{space 3} .1505571
{txt}{space 11}migration_total2019 {c |}{col 32}{res}{space 2} .0010743{col 44}{space 2} .0114828{col 55}{space 1}    0.09{col 64}{space 3}0.925{col 72}{space 4}-.0214316{col 85}{space 3} .0235803
{txt}{space 23}log_gdp {c |}{col 32}{res}{space 2}-.1130636{col 44}{space 2}  .110873{col 55}{space 1}   -1.02{col 64}{space 3}0.308{col 72}{space 4}-.3303706{col 85}{space 3} .1042435
{txt}{space 20}log_gdp_pc {c |}{col 32}{res}{space 2}-.0189247{col 44}{space 2} .1899952{col 55}{space 1}   -0.10{col 64}{space 3}0.921{col 72}{space 4}-.3913084{col 85}{space 3}  .353459
{txt}{space 15}log_resource_pc {c |}{col 32}{res}{space 2} .1885611{col 44}{space 2} .0819356{col 55}{space 1}    2.30{col 64}{space 3}0.021{col 72}{space 4} .0279704{col 85}{space 3} .3491519
{txt}{space 23}polity2 {c |}{col 32}{res}{space 2} .0088539{col 44}{space 2} .0305879{col 55}{space 1}    0.29{col 64}{space 3}0.772{col 72}{space 4}-.0510973{col 85}{space 3} .0688052
{txt}{space 19}health_2018 {c |}{col 32}{res}{space 2}-.0198147{col 44}{space 2} .0847545{col 55}{space 1}   -0.23{col 64}{space 3}0.815{col 72}{space 4}-.1859304{col 85}{space 3}  .146301
{txt}{space 17}distcap_china {c |}{col 32}{res}{space 2}-.1036094{col 44}{space 2} .0475643{col 55}{space 1}   -2.18{col 64}{space 3}0.029{col 72}{space 4}-.1968337{col 85}{space 3}-.0103851
{txt}{space 15}deaths_7_day_pc {c |}{col 32}{res}{space 2}-25.20377{col 44}{space 2} 16.17282{col 55}{space 1}   -1.56{col 64}{space 3}0.119{col 72}{space 4} -56.9019{col 85}{space 3}  6.49437
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. eststo: stcox intl_student_pc_china2018 migration_from_china_pc2019 migration_total2019 log_gdp log_gdp_pc log_resource_pc polity2 health_2018 distcap_china deaths_7_day_pc, nohr vce(robust)

         {txt}failure _d:  {res}be_cat_1_China_carryover
   {txt}analysis time _t:  {res}date
                 {txt}id:  {res}cowcode

{txt}Iteration 0:   log pseudolikelihood = {res}-268.22726
{txt}Iteration 1:   log pseudolikelihood = {res}-261.57026
{txt}Iteration 2:   log pseudolikelihood = {res} -258.8577
{txt}Iteration 3:   log pseudolikelihood = {res}-256.43762
{txt}Iteration 4:   log pseudolikelihood = {res}-255.20016
{txt}Iteration 5:   log pseudolikelihood = {res}-254.49829
{txt}Iteration 6:   log pseudolikelihood = {res}-254.16311
{txt}Iteration 7:   log pseudolikelihood = {res}-254.10569
{txt}Iteration 8:   log pseudolikelihood = {res}-254.10379
{txt}Iteration 9:   log pseudolikelihood = {res}-254.10379
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res}-254.10379

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         148             {txt}Number of obs    =  {res}     8,951
{txt}No. of failures      = {res}          56
{txt}Time at risk         = {res}     3254295
                                                {txt}Wald chi2({res}10{txt})    =  {res}     35.46
{txt}Log pseudolikelihood =   {res}-254.10379             {txt}Prob > chi2      =  {res}    0.0001

{txt}{ralign 93:(Std. Err. adjusted for {res:148} clusters in cowcode)}
{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                         _t{col 29}{c |}      Coef.{col 41}   Std. Err.{col 53}      z{col 61}   P>|z|{col 69}     [95% Con{col 82}f. Interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}intl_student_pc_china2018 {c |}{col 29}{res}{space 2}-.0159331{col 41}{space 2} .1726653{col 52}{space 1}   -0.09{col 61}{space 3}0.926{col 69}{space 4}-.3543509{col 82}{space 3} .3224847
{txt}migration_from_china_pc2019 {c |}{col 29}{res}{space 2} .1000867{col 41}{space 2} .0230186{col 52}{space 1}    4.35{col 61}{space 3}0.000{col 69}{space 4} .0549711{col 82}{space 3} .1452022
{txt}{space 8}migration_total2019 {c |}{col 29}{res}{space 2} .0010559{col 41}{space 2} .0114961{col 52}{space 1}    0.09{col 61}{space 3}0.927{col 69}{space 4} -.021476{col 82}{space 3} .0235877
{txt}{space 20}log_gdp {c |}{col 29}{res}{space 2}-.1273163{col 41}{space 2} .1120093{col 52}{space 1}   -1.14{col 61}{space 3}0.256{col 69}{space 4}-.3468505{col 82}{space 3} .0922178
{txt}{space 17}log_gdp_pc {c |}{col 29}{res}{space 2} .0005531{col 41}{space 2} .1918092{col 52}{space 1}    0.00{col 61}{space 3}0.998{col 69}{space 4}-.3753861{col 82}{space 3} .3764923
{txt}{space 12}log_resource_pc {c |}{col 29}{res}{space 2} .1682315{col 41}{space 2} .0816312{col 52}{space 1}    2.06{col 61}{space 3}0.039{col 69}{space 4} .0082372{col 82}{space 3} .3282258
{txt}{space 20}polity2 {c |}{col 29}{res}{space 2} .0031141{col 41}{space 2} .0306354{col 52}{space 1}    0.10{col 61}{space 3}0.919{col 69}{space 4}-.0569301{col 82}{space 3} .0631583
{txt}{space 16}health_2018 {c |}{col 29}{res}{space 2}-.0359087{col 41}{space 2} .0858253{col 52}{space 1}   -0.42{col 61}{space 3}0.676{col 69}{space 4}-.2041231{col 82}{space 3} .1323058
{txt}{space 14}distcap_china {c |}{col 29}{res}{space 2}-.1009014{col 41}{space 2} .0485334{col 52}{space 1}   -2.08{col 61}{space 3}0.038{col 69}{space 4}-.1960251{col 82}{space 3}-.0057778
{txt}{space 12}deaths_7_day_pc {c |}{col 29}{res}{space 2}-25.70005{col 41}{space 2} 16.43748{col 52}{space 1}   -1.56{col 61}{space 3}0.118{col 69}{space 4}-57.91691{col 82}{space 3} 6.516805
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}. eststo: stcox contract_workers_abroad_2018_pc labor_services_workers_2018_pc intl_student_pc_china2018 migration_from_china_pc2019 migration_total2019 log_gdp log_gdp_pc log_resource_pc polity2 health_2018 distcap_china deaths_7_day_pc, nohr vce(robust)

         {txt}failure _d:  {res}be_cat_1_China_carryover
   {txt}analysis time _t:  {res}date
                 {txt}id:  {res}cowcode

{txt}Iteration 0:   log pseudolikelihood = {res}-268.22726
{txt}Iteration 1:   log pseudolikelihood = {res}-265.33595
{txt}Iteration 2:   log pseudolikelihood = {res}-259.24517
{txt}Iteration 3:   log pseudolikelihood = {res}-255.38516
{txt}Iteration 4:   log pseudolikelihood = {res}-253.18504
{txt}Iteration 5:   log pseudolikelihood = {res}-252.15978
{txt}Iteration 6:   log pseudolikelihood = {res}-251.46553
{txt}Iteration 7:   log pseudolikelihood = {res}-251.23015
{txt}Iteration 8:   log pseudolikelihood = {res}-251.20229
{txt}Iteration 9:   log pseudolikelihood = {res}-251.20183
{txt}Iteration 10:  log pseudolikelihood = {res}-251.20183
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res}-251.20183

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         148             {txt}Number of obs    =  {res}     8,951
{txt}No. of failures      = {res}          56
{txt}Time at risk         = {res}     3254295
                                                {txt}Wald chi2({res}12{txt})    =  {res}     69.38
{txt}Log pseudolikelihood =   {res}-251.20183             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 97:(Std. Err. adjusted for {res:148} clusters in cowcode)}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                             _t{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      z{col 65}   P>|z|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
contract_workers_abroad_2018_pc {c |}{col 33}{res}{space 2} .7704314{col 45}{space 2} .2165817{col 56}{space 1}    3.56{col 65}{space 3}0.000{col 73}{space 4} .3459391{col 86}{space 3} 1.194924
{txt}{space 1}labor_services_workers_2018_pc {c |}{col 33}{res}{space 2} .1907741{col 45}{space 2} .1768886{col 56}{space 1}    1.08{col 65}{space 3}0.281{col 73}{space 4}-.1559212{col 86}{space 3} .5374694
{txt}{space 6}intl_student_pc_china2018 {c |}{col 33}{res}{space 2} .3235171{col 45}{space 2} .2897016{col 56}{space 1}    1.12{col 65}{space 3}0.264{col 73}{space 4}-.2442876{col 86}{space 3} .8913219
{txt}{space 4}migration_from_china_pc2019 {c |}{col 33}{res}{space 2} .0446212{col 45}{space 2} .0605403{col 56}{space 1}    0.74{col 65}{space 3}0.461{col 73}{space 4}-.0740356{col 86}{space 3} .1632781
{txt}{space 12}migration_total2019 {c |}{col 33}{res}{space 2}-.0056033{col 45}{space 2} .0104798{col 56}{space 1}   -0.53{col 65}{space 3}0.593{col 73}{space 4}-.0261434{col 86}{space 3} .0149367
{txt}{space 24}log_gdp {c |}{col 33}{res}{space 2}-.0892459{col 45}{space 2}  .109304{col 56}{space 1}   -0.82{col 65}{space 3}0.414{col 73}{space 4}-.3034778{col 86}{space 3}  .124986
{txt}{space 21}log_gdp_pc {c |}{col 33}{res}{space 2}-.0090465{col 45}{space 2} .1774612{col 56}{space 1}   -0.05{col 65}{space 3}0.959{col 73}{space 4}-.3568641{col 86}{space 3} .3387712
{txt}{space 16}log_resource_pc {c |}{col 33}{res}{space 2} .1401787{col 45}{space 2} .0841656{col 56}{space 1}    1.67{col 65}{space 3}0.096{col 73}{space 4}-.0247828{col 86}{space 3} .3051401
{txt}{space 24}polity2 {c |}{col 33}{res}{space 2} .0110475{col 45}{space 2} .0304139{col 56}{space 1}    0.36{col 65}{space 3}0.716{col 73}{space 4}-.0485627{col 86}{space 3} .0706577
{txt}{space 20}health_2018 {c |}{col 33}{res}{space 2}-.0164913{col 45}{space 2} .0859011{col 56}{space 1}   -0.19{col 65}{space 3}0.848{col 73}{space 4}-.1848543{col 86}{space 3} .1518718
{txt}{space 18}distcap_china {c |}{col 33}{res}{space 2}-.1052961{col 45}{space 2} .0471856{col 56}{space 1}   -2.23{col 65}{space 3}0.026{col 73}{space 4}-.1977782{col 86}{space 3} -.012814
{txt}{space 16}deaths_7_day_pc {c |}{col 33}{res}{space 2}-25.95814{col 45}{space 2} 16.86879{col 56}{space 1}   -1.54{col 65}{space 3}0.124{col 73}{space 4}-59.02036{col 86}{space 3} 7.104079
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{com}. eststo: stcox contract_workers_abroad_2018_pc labor_services_workers_2018_pc intl_student_pc_china2018 migration_from_china_pc2019 migration_total2019 log_gdp log_gdp_pc log_resource_pc polity2 health_2018 distcap_china fdi_china_inward_pc_2018 trade_china_gdp_2018 deaths_7_day_pc, nohr vce(robust)

         {txt}failure _d:  {res}be_cat_1_China_carryover
   {txt}analysis time _t:  {res}date
                 {txt}id:  {res}cowcode

{txt}Iteration 0:   log pseudolikelihood = {res}-207.18337
{txt}Iteration 1:   log pseudolikelihood = {res}-204.81414
{txt}Iteration 2:   log pseudolikelihood = {res}-202.06535
{txt}Iteration 3:   log pseudolikelihood = {res}-199.11808
{txt}Iteration 4:   log pseudolikelihood = {res}-195.63308
{txt}Iteration 5:   log pseudolikelihood = {res}-193.46301
{txt}Iteration 6:   log pseudolikelihood = {res}-193.01318
{txt}Iteration 7:   log pseudolikelihood = {res}-192.36998
{txt}Iteration 8:   log pseudolikelihood = {res} -191.7114
{txt}Iteration 9:   log pseudolikelihood = {res}-191.42427
{txt}Iteration 10:  log pseudolikelihood = {res} -191.3877
{txt}Iteration 11:  log pseudolikelihood = {res}-191.38691
{txt}Iteration 12:  log pseudolikelihood = {res}-191.38691
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res}-191.38691

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         122             {txt}Number of obs    =  {res}     7,402
{txt}No. of failures      = {res}          45
{txt}Time at risk         = {res}     2682618
                                                {txt}Wald chi2({res}14{txt})    =  {res}     55.97
{txt}Log pseudolikelihood =   {res}-191.38691             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 97:(Std. Err. adjusted for {res:122} clusters in cowcode)}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                             _t{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      z{col 65}   P>|z|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
contract_workers_abroad_2018_pc {c |}{col 33}{res}{space 2} .8894512{col 45}{space 2} .3682511{col 56}{space 1}    2.42{col 65}{space 3}0.016{col 73}{space 4} .1676922{col 86}{space 3}  1.61121
{txt}{space 1}labor_services_workers_2018_pc {c |}{col 33}{res}{space 2} .5926266{col 45}{space 2} .4662497{col 56}{space 1}    1.27{col 65}{space 3}0.204{col 73}{space 4}-.3212061{col 86}{space 3} 1.506459
{txt}{space 6}intl_student_pc_china2018 {c |}{col 33}{res}{space 2} .6955403{col 45}{space 2} .4696744{col 56}{space 1}    1.48{col 65}{space 3}0.139{col 73}{space 4}-.2250045{col 86}{space 3} 1.616085
{txt}{space 4}migration_from_china_pc2019 {c |}{col 33}{res}{space 2}-.0136895{col 45}{space 2} .0975685{col 56}{space 1}   -0.14{col 65}{space 3}0.888{col 73}{space 4}-.2049202{col 86}{space 3} .1775412
{txt}{space 12}migration_total2019 {c |}{col 33}{res}{space 2}-.0078251{col 45}{space 2} .0161966{col 56}{space 1}   -0.48{col 65}{space 3}0.629{col 73}{space 4}  -.03957{col 86}{space 3} .0239197
{txt}{space 24}log_gdp {c |}{col 33}{res}{space 2}-.0234003{col 45}{space 2}  .128409{col 56}{space 1}   -0.18{col 65}{space 3}0.855{col 73}{space 4}-.2750772{col 86}{space 3} .2282767
{txt}{space 21}log_gdp_pc {c |}{col 33}{res}{space 2}-.0028423{col 45}{space 2} .2302907{col 56}{space 1}   -0.01{col 65}{space 3}0.990{col 73}{space 4}-.4542038{col 86}{space 3} .4485191
{txt}{space 16}log_resource_pc {c |}{col 33}{res}{space 2} .0837042{col 45}{space 2} .1008145{col 56}{space 1}    0.83{col 65}{space 3}0.406{col 73}{space 4}-.1138886{col 86}{space 3}  .281297
{txt}{space 24}polity2 {c |}{col 33}{res}{space 2}-.0214223{col 45}{space 2}  .037982{col 56}{space 1}   -0.56{col 65}{space 3}0.573{col 73}{space 4}-.0958658{col 86}{space 3} .0530211
{txt}{space 20}health_2018 {c |}{col 33}{res}{space 2} .0372589{col 45}{space 2} .1103755{col 56}{space 1}    0.34{col 65}{space 3}0.736{col 73}{space 4}-.1790731{col 86}{space 3} .2535908
{txt}{space 18}distcap_china {c |}{col 33}{res}{space 2}-.0812771{col 45}{space 2} .0508516{col 56}{space 1}   -1.60{col 65}{space 3}0.110{col 73}{space 4}-.1809443{col 86}{space 3} .0183901
{txt}{space 7}fdi_china_inward_pc_2018 {c |}{col 33}{res}{space 2}-.0004796{col 45}{space 2} .0007303{col 56}{space 1}   -0.66{col 65}{space 3}0.511{col 73}{space 4} -.001911{col 86}{space 3} .0009518
{txt}{space 11}trade_china_gdp_2018 {c |}{col 33}{res}{space 2} .0106815{col 45}{space 2} .0147341{col 56}{space 1}    0.72{col 65}{space 3}0.468{col 73}{space 4}-.0181967{col 86}{space 3} .0395598
{txt}{space 16}deaths_7_day_pc {c |}{col 33}{res}{space 2}-25.78494{col 45}{space 2} 17.33567{col 56}{space 1}   -1.49{col 65}{space 3}0.137{col 73}{space 4}-59.76222{col 86}{space 3} 8.192346
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{com}. eststo: stcox contract_workers_abroad_2018_pc labor_services_workers_2018_pc intl_student_pc_china2018 migration_from_china_pc2019 migration_total2019 log_gdp log_gdp_pc log_resource_pc polity2 health_2018 distcap_china fdi_china_inward_pc_2018 trade_china_gdp_2018 trade_nonchina_gdp_2018 deaths_7_day_pc, nohr vce(robust)

         {txt}failure _d:  {res}be_cat_1_China_carryover
   {txt}analysis time _t:  {res}date
                 {txt}id:  {res}cowcode

{txt}Iteration 0:   log pseudolikelihood = {res}-207.18337
{txt}Iteration 1:   log pseudolikelihood = {res}-204.31563
{txt}Iteration 2:   log pseudolikelihood = {res}-203.20872
{txt}Iteration 3:   log pseudolikelihood = {res}-197.68314
{txt}Iteration 4:   log pseudolikelihood = {res}-196.71613
{txt}Iteration 5:   log pseudolikelihood = {res}-193.89346
{txt}Iteration 6:   log pseudolikelihood = {res} -192.1842
{txt}Iteration 7:   log pseudolikelihood = {res}-191.49446
{txt}Iteration 8:   log pseudolikelihood = {res}-190.75511
{txt}Iteration 9:   log pseudolikelihood = {res}-190.10006
{txt}Iteration 10:  log pseudolikelihood = {res}-189.86842
{txt}Iteration 11:  log pseudolikelihood = {res}-189.84261
{txt}Iteration 12:  log pseudolikelihood = {res}-189.84222
{txt}Iteration 13:  log pseudolikelihood = {res}-189.84222
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res}-189.84222

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         122             {txt}Number of obs    =  {res}     7,402
{txt}No. of failures      = {res}          45
{txt}Time at risk         = {res}     2682618
                                                {txt}Wald chi2({res}15{txt})    =  {res}     63.89
{txt}Log pseudolikelihood =   {res}-189.84222             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 97:(Std. Err. adjusted for {res:122} clusters in cowcode)}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                             _t{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      z{col 65}   P>|z|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
contract_workers_abroad_2018_pc {c |}{col 33}{res}{space 2} .8502825{col 45}{space 2} .3396883{col 56}{space 1}    2.50{col 65}{space 3}0.012{col 73}{space 4} .1845056{col 86}{space 3} 1.516059
{txt}{space 1}labor_services_workers_2018_pc {c |}{col 33}{res}{space 2} .4805998{col 45}{space 2} .3600406{col 56}{space 1}    1.33{col 65}{space 3}0.182{col 73}{space 4}-.2250667{col 86}{space 3} 1.186266
{txt}{space 6}intl_student_pc_china2018 {c |}{col 33}{res}{space 2} .5173784{col 45}{space 2} .4694089{col 56}{space 1}    1.10{col 65}{space 3}0.270{col 73}{space 4}-.4026461{col 86}{space 3} 1.437403
{txt}{space 4}migration_from_china_pc2019 {c |}{col 33}{res}{space 2} -.004167{col 45}{space 2}  .096361{col 56}{space 1}   -0.04{col 65}{space 3}0.966{col 73}{space 4}-.1930311{col 86}{space 3} .1846971
{txt}{space 12}migration_total2019 {c |}{col 33}{res}{space 2}-.0076632{col 45}{space 2} .0149755{col 56}{space 1}   -0.51{col 65}{space 3}0.609{col 73}{space 4}-.0370147{col 86}{space 3} .0216882
{txt}{space 24}log_gdp {c |}{col 33}{res}{space 2}-.1003788{col 45}{space 2} .1375836{col 56}{space 1}   -0.73{col 65}{space 3}0.466{col 73}{space 4}-.3700378{col 86}{space 3} .1692801
{txt}{space 21}log_gdp_pc {c |}{col 33}{res}{space 2} .1554274{col 45}{space 2} .2469301{col 56}{space 1}    0.63{col 65}{space 3}0.529{col 73}{space 4}-.3285467{col 86}{space 3} .6394014
{txt}{space 16}log_resource_pc {c |}{col 33}{res}{space 2} .0633972{col 45}{space 2} .0917013{col 56}{space 1}    0.69{col 65}{space 3}0.489{col 73}{space 4} -.116334{col 86}{space 3} .2431284
{txt}{space 24}polity2 {c |}{col 33}{res}{space 2}-.0289204{col 45}{space 2}    .0366{col 56}{space 1}   -0.79{col 65}{space 3}0.429{col 73}{space 4} -.100655{col 86}{space 3} .0428143
{txt}{space 20}health_2018 {c |}{col 33}{res}{space 2} .0327367{col 45}{space 2} .1047051{col 56}{space 1}    0.31{col 65}{space 3}0.755{col 73}{space 4}-.1724816{col 86}{space 3}  .237955
{txt}{space 18}distcap_china {c |}{col 33}{res}{space 2}-.0970754{col 45}{space 2} .0458652{col 56}{space 1}   -2.12{col 65}{space 3}0.034{col 73}{space 4}-.1869695{col 86}{space 3}-.0071812
{txt}{space 7}fdi_china_inward_pc_2018 {c |}{col 33}{res}{space 2}-.0002274{col 45}{space 2} .0004963{col 56}{space 1}   -0.46{col 65}{space 3}0.647{col 73}{space 4}-.0012001{col 86}{space 3} .0007453
{txt}{space 11}trade_china_gdp_2018 {c |}{col 33}{res}{space 2} .0141767{col 45}{space 2} .0141511{col 56}{space 1}    1.00{col 65}{space 3}0.316{col 73}{space 4}-.0135589{col 86}{space 3} .0419124
{txt}{space 8}trade_nonchina_gdp_2018 {c |}{col 33}{res}{space 2}-.0106395{col 45}{space 2} .0069571{col 56}{space 1}   -1.53{col 65}{space 3}0.126{col 73}{space 4}-.0242751{col 86}{space 3} .0029961
{txt}{space 16}deaths_7_day_pc {c |}{col 33}{res}{space 2}-27.61428{col 45}{space 2} 18.10902{col 56}{space 1}   -1.52{col 65}{space 3}0.127{col 73}{space 4}-63.10731{col 86}{space 3} 7.878754
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est6{txt} stored)

{com}. 
. esttab using "Table1.tex", label keep(contract_workers_abroad_2018_pc labor_services_workers_2018_pc intl_student_pc_china2018 migration_from_china_pc2019 migration_total2019 fdi_china_inward_pc_2018 log_gdp log_gdp_pc trade_china_gdp_2018 trade_nonchina_gdp_2018 log_resource_pc polity2 health_2018 distcap_china deaths_7_day_pc) order(contract_workers_abroad_2018_pc labor_services_workers_2018_pc intl_student_pc_china2018 migration_from_china_pc2019 migration_total2019 fdi_china_inward_pc_2018 log_gdp log_gdp_pc trade_china_gdp_2018 trade_nonchina_gdp_2018 log_resource_pc polity2 health_2018 distcap_china deaths_7_day_pc) nonotes se(3) b(3) replace star(+ 0.10 * 0.05 ** 0.01) compress nogaps nodepvars mtitles("" "" "" "" "" "") 
{res}{txt}(note: file Table1.tex not found)
(output written to {browse  `"Table1.tex"'})

{com}. 
. * Table 2: Any Category 1 Restrictions
. 
. stset date, id (cowcode) failure (be_any_1_China_carryover)

                {txt}id:  {res}cowcode
     {txt}failure event:  {res}be_any_1_China_carryover != 0 & be_any_1_China_carryover < .
{txt}obs. time interval:  {res}(date[_n-1], date]
{txt} exit on or before:  {res}failure

{txt}{hline 78}
{res}     14,235{txt}  total observations
{res}      3,603{txt}  observations begin on or after (first) failure
{hline 78}
{res}     10,632{txt}  observations remaining, representing
{res}        195{txt}  subjects
{res}         89{txt}  failures in single-failure-per-subject data
{res}  4,286,592{txt}  total analysis time at risk and under observation
                                                at risk from t = {res}        0
                                     {txt}earliest observed entry t = {res}        0
                                          {txt}last observed exit t = {res}   22,001
{txt}
{com}. stsum

         {txt}failure _d:  {res}be_any_1_China_carryover
   {txt}analysis time _t:  {res}date
                 {txt}id:  {res}cowcode

{txt}{col 10}{c |}{col 26}incidence{col 42}no. of{col 52}{c LT}{hline 6} Survival time {hline 5}{c RT}
{col 10}{c |} time at risk{col 29}rate{col 41}subjects{col 57}25%{col 67}50%{col 77}75%
{hline 9}{c +}{hline 69}
   total {c |} {res}     4286592   .0000208{col 40}      195      21952         .         .
{txt}
{com}. 
. eststo clear 
{txt}
{com}. eststo: stcox contract_workers_abroad_2018_pc migration_from_china_pc2019 migration_total2019 log_gdp log_gdp_pc log_resource_pc polity2 health_2018 distcap_china deaths_7_day_pc, nohr vce(robust)

         {txt}failure _d:  {res}be_any_1_China_carryover
   {txt}analysis time _t:  {res}date
                 {txt}id:  {res}cowcode

{txt}Iteration 0:   log pseudolikelihood = {res}-317.77679
{txt}Iteration 1:   log pseudolikelihood = {res}-306.96231
{txt}Iteration 2:   log pseudolikelihood = {res}-304.96248
{txt}Iteration 3:   log pseudolikelihood = {res}-304.54493
{txt}Iteration 4:   log pseudolikelihood = {res}-304.28777
{txt}Iteration 5:   log pseudolikelihood = {res}-304.24286
{txt}Iteration 6:   log pseudolikelihood = {res}-304.24175
{txt}Iteration 7:   log pseudolikelihood = {res}-304.24175
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res}-304.24175

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         148             {txt}Number of obs    =  {res}     8,267
{txt}No. of failures      = {res}          67
{txt}Time at risk         = {res}     3253611
                                                {txt}Wald chi2({res}10{txt})    =  {res}     47.67
{txt}Log pseudolikelihood =   {res}-304.24175             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 97:(Std. Err. adjusted for {res:148} clusters in cowcode)}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                             _t{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      z{col 65}   P>|z|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
contract_workers_abroad_2018_pc {c |}{col 33}{res}{space 2}  .531612{col 45}{space 2} .2795885{col 56}{space 1}    1.90{col 65}{space 3}0.057{col 73}{space 4}-.0163713{col 86}{space 3} 1.079595
{txt}{space 4}migration_from_china_pc2019 {c |}{col 33}{res}{space 2} .0748646{col 45}{space 2} .0184186{col 56}{space 1}    4.06{col 65}{space 3}0.000{col 73}{space 4} .0387647{col 86}{space 3} .1109645
{txt}{space 12}migration_total2019 {c |}{col 33}{res}{space 2}-.0014597{col 45}{space 2} .0110363{col 56}{space 1}   -0.13{col 65}{space 3}0.895{col 73}{space 4}-.0230903{col 86}{space 3}  .020171
{txt}{space 24}log_gdp {c |}{col 33}{res}{space 2} .0378185{col 45}{space 2} .0971292{col 56}{space 1}    0.39{col 65}{space 3}0.697{col 73}{space 4}-.1525512{col 86}{space 3} .2281881
{txt}{space 21}log_gdp_pc {c |}{col 33}{res}{space 2}-.0553801{col 45}{space 2}  .172974{col 56}{space 1}   -0.32{col 65}{space 3}0.749{col 73}{space 4}-.3944029{col 86}{space 3} .2836427
{txt}{space 16}log_resource_pc {c |}{col 33}{res}{space 2} .1060719{col 45}{space 2} .0668655{col 56}{space 1}    1.59{col 65}{space 3}0.113{col 73}{space 4}-.0249822{col 86}{space 3} .2371259
{txt}{space 24}polity2 {c |}{col 33}{res}{space 2}-.0223796{col 45}{space 2} .0293514{col 56}{space 1}   -0.76{col 65}{space 3}0.446{col 73}{space 4}-.0799073{col 86}{space 3} .0351482
{txt}{space 20}health_2018 {c |}{col 33}{res}{space 2}-.0320042{col 45}{space 2} .0694832{col 56}{space 1}   -0.46{col 65}{space 3}0.645{col 73}{space 4}-.1681887{col 86}{space 3} .1041803
{txt}{space 18}distcap_china {c |}{col 33}{res}{space 2}-.0604937{col 45}{space 2}  .041789{col 56}{space 1}   -1.45{col 65}{space 3}0.148{col 73}{space 4}-.1423986{col 86}{space 3} .0214111
{txt}{space 16}deaths_7_day_pc {c |}{col 33}{res}{space 2}-12.38537{col 45}{space 2} 11.11317{col 56}{space 1}   -1.11{col 65}{space 3}0.265{col 73}{space 4}-34.16679{col 86}{space 3} 9.396055
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}. eststo: stcox labor_services_workers_2018_pc migration_from_china_pc2019 migration_total2019 log_gdp log_gdp_pc log_resource_pc polity2 health_2018 distcap_china deaths_7_day_pc, nohr vce(robust)

         {txt}failure _d:  {res}be_any_1_China_carryover
   {txt}analysis time _t:  {res}date
                 {txt}id:  {res}cowcode

{txt}Iteration 0:   log pseudolikelihood = {res}-317.77679
{txt}Iteration 1:   log pseudolikelihood = {res}-315.98165
{txt}Iteration 2:   log pseudolikelihood = {res}-309.42132
{txt}Iteration 3:   log pseudolikelihood = {res} -306.1462
{txt}Iteration 4:   log pseudolikelihood = {res} -305.0563
{txt}Iteration 5:   log pseudolikelihood = {res}-304.78747
{txt}Iteration 6:   log pseudolikelihood = {res}-304.73583
{txt}Iteration 7:   log pseudolikelihood = {res} -304.7343
{txt}Iteration 8:   log pseudolikelihood = {res} -304.7343
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res} -304.7343

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         148             {txt}Number of obs    =  {res}     8,267
{txt}No. of failures      = {res}          67
{txt}Time at risk         = {res}     3253611
                                                {txt}Wald chi2({res}10{txt})    =  {res}     61.76
{txt}Log pseudolikelihood =   {res} -304.7343             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 96:(Std. Err. adjusted for {res:148} clusters in cowcode)}
{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}    Robust
{col 1}                            _t{col 32}{c |}      Coef.{col 44}   Std. Err.{col 56}      z{col 64}   P>|z|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
labor_services_workers_2018_pc {c |}{col 32}{res}{space 2} .1387054{col 44}{space 2} .1034334{col 55}{space 1}    1.34{col 64}{space 3}0.180{col 72}{space 4}-.0640204{col 85}{space 3} .3414312
{txt}{space 3}migration_from_china_pc2019 {c |}{col 32}{res}{space 2} .0458392{col 44}{space 2}  .032248{col 55}{space 1}    1.42{col 64}{space 3}0.155{col 72}{space 4}-.0173657{col 85}{space 3} .1090442
{txt}{space 11}migration_total2019 {c |}{col 32}{res}{space 2} .0054792{col 44}{space 2} .0095079{col 55}{space 1}    0.58{col 64}{space 3}0.564{col 72}{space 4} -.013156{col 85}{space 3} .0241143
{txt}{space 23}log_gdp {c |}{col 32}{res}{space 2} .0411953{col 44}{space 2} .0995662{col 55}{space 1}    0.41{col 64}{space 3}0.679{col 72}{space 4} -.153951{col 85}{space 3} .2363415
{txt}{space 20}log_gdp_pc {c |}{col 32}{res}{space 2}-.1044252{col 44}{space 2} .1759191{col 55}{space 1}   -0.59{col 64}{space 3}0.553{col 72}{space 4}-.4492203{col 85}{space 3} .2403699
{txt}{space 15}log_resource_pc {c |}{col 32}{res}{space 2} .1517421{col 44}{space 2} .0709557{col 55}{space 1}    2.14{col 64}{space 3}0.032{col 72}{space 4} .0126716{col 85}{space 3} .2908127
{txt}{space 23}polity2 {c |}{col 32}{res}{space 2}-.0162256{col 44}{space 2} .0299628{col 55}{space 1}   -0.54{col 64}{space 3}0.588{col 72}{space 4}-.0749517{col 85}{space 3} .0425004
{txt}{space 19}health_2018 {c |}{col 32}{res}{space 2}-.0160196{col 44}{space 2} .0699211{col 55}{space 1}   -0.23{col 64}{space 3}0.819{col 72}{space 4}-.1530625{col 85}{space 3} .1210233
{txt}{space 17}distcap_china {c |}{col 32}{res}{space 2}-.0695406{col 44}{space 2}  .042181{col 55}{space 1}   -1.65{col 64}{space 3}0.099{col 72}{space 4}-.1522138{col 85}{space 3} .0131325
{txt}{space 15}deaths_7_day_pc {c |}{col 32}{res}{space 2}-12.12052{col 44}{space 2} 10.77089{col 55}{space 1}   -1.13{col 64}{space 3}0.260{col 72}{space 4}-33.23108{col 85}{space 3}  8.99004
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. eststo: stcox intl_student_pc_china2018 migration_from_china_pc2019 migration_total2019 log_gdp log_gdp_pc log_resource_pc polity2 health_2018 distcap_china deaths_7_day_pc, nohr vce(robust)

         {txt}failure _d:  {res}be_any_1_China_carryover
   {txt}analysis time _t:  {res}date
                 {txt}id:  {res}cowcode

{txt}Iteration 0:   log pseudolikelihood = {res}-317.77679
{txt}Iteration 1:   log pseudolikelihood = {res}-309.96887
{txt}Iteration 2:   log pseudolikelihood = {res}-306.03578
{txt}Iteration 3:   log pseudolikelihood = {res}-305.54383
{txt}Iteration 4:   log pseudolikelihood = {res}-305.28445
{txt}Iteration 5:   log pseudolikelihood = {res}  -305.239
{txt}Iteration 6:   log pseudolikelihood = {res}-305.23783
{txt}Iteration 7:   log pseudolikelihood = {res}-305.23783
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res}-305.23783

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         148             {txt}Number of obs    =  {res}     8,267
{txt}No. of failures      = {res}          67
{txt}Time at risk         = {res}     3253611
                                                {txt}Wald chi2({res}10{txt})    =  {res}     49.71
{txt}Log pseudolikelihood =   {res}-305.23783             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 93:(Std. Err. adjusted for {res:148} clusters in cowcode)}
{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                         _t{col 29}{c |}      Coef.{col 41}   Std. Err.{col 53}      z{col 61}   P>|z|{col 69}     [95% Con{col 82}f. Interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}intl_student_pc_china2018 {c |}{col 29}{res}{space 2}-.1256283{col 41}{space 2} .1770368{col 52}{space 1}   -0.71{col 61}{space 3}0.478{col 69}{space 4}-.4726141{col 82}{space 3} .2213575
{txt}migration_from_china_pc2019 {c |}{col 29}{res}{space 2} .0807144{col 41}{space 2} .0196801{col 52}{space 1}    4.10{col 61}{space 3}0.000{col 69}{space 4} .0421421{col 82}{space 3} .1192866
{txt}{space 8}migration_total2019 {c |}{col 29}{res}{space 2} .0056484{col 41}{space 2} .0095092{col 52}{space 1}    0.59{col 61}{space 3}0.553{col 69}{space 4}-.0129893{col 82}{space 3}  .024286
{txt}{space 20}log_gdp {c |}{col 29}{res}{space 2} .0328035{col 41}{space 2} .1000576{col 52}{space 1}    0.33{col 61}{space 3}0.743{col 69}{space 4}-.1633057{col 82}{space 3} .2289128
{txt}{space 17}log_gdp_pc {c |}{col 29}{res}{space 2}-.0918068{col 41}{space 2} .1769483{col 52}{space 1}   -0.52{col 61}{space 3}0.604{col 69}{space 4} -.438619{col 82}{space 3} .2550055
{txt}{space 12}log_resource_pc {c |}{col 29}{res}{space 2} .1389295{col 41}{space 2} .0710656{col 52}{space 1}    1.95{col 61}{space 3}0.051{col 69}{space 4}-.0003565{col 82}{space 3} .2782155
{txt}{space 20}polity2 {c |}{col 29}{res}{space 2}-.0199762{col 41}{space 2}  .030008{col 52}{space 1}   -0.67{col 61}{space 3}0.506{col 69}{space 4}-.0787909{col 82}{space 3} .0388385
{txt}{space 16}health_2018 {c |}{col 29}{res}{space 2}-.0281379{col 41}{space 2}  .070204{col 52}{space 1}   -0.40{col 61}{space 3}0.689{col 69}{space 4}-.1657353{col 82}{space 3} .1094594
{txt}{space 14}distcap_china {c |}{col 29}{res}{space 2}-.0658793{col 41}{space 2} .0427199{col 52}{space 1}   -1.54{col 61}{space 3}0.123{col 69}{space 4}-.1496088{col 82}{space 3} .0178502
{txt}{space 12}deaths_7_day_pc {c |}{col 29}{res}{space 2} -12.3257{col 41}{space 2} 10.86719{col 52}{space 1}   -1.13{col 61}{space 3}0.257{col 69}{space 4}  -33.625{col 82}{space 3} 8.973607
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}. eststo: stcox contract_workers_abroad_2018_pc labor_services_workers_2018_pc intl_student_pc_china2018 migration_from_china_pc2019 migration_total2019 log_gdp log_gdp_pc log_resource_pc polity2 health_2018 distcap_china deaths_7_day_pc, nohr vce(robust)

         {txt}failure _d:  {res}be_any_1_China_carryover
   {txt}analysis time _t:  {res}date
                 {txt}id:  {res}cowcode

{txt}Iteration 0:   log pseudolikelihood = {res}-317.77679
{txt}Iteration 1:   log pseudolikelihood = {res}-316.82024
{txt}Iteration 2:   log pseudolikelihood = {res}-308.29242
{txt}Iteration 3:   log pseudolikelihood = {res}-305.32933
{txt}Iteration 4:   log pseudolikelihood = {res}-304.25662
{txt}Iteration 5:   log pseudolikelihood = {res}-303.91713
{txt}Iteration 6:   log pseudolikelihood = {res}-303.81398
{txt}Iteration 7:   log pseudolikelihood = {res}-303.80779
{txt}Iteration 8:   log pseudolikelihood = {res}-303.80777
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res}-303.80777

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         148             {txt}Number of obs    =  {res}     8,267
{txt}No. of failures      = {res}          67
{txt}Time at risk         = {res}     3253611
                                                {txt}Wald chi2({res}12{txt})    =  {res}     64.50
{txt}Log pseudolikelihood =   {res}-303.80777             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 97:(Std. Err. adjusted for {res:148} clusters in cowcode)}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                             _t{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      z{col 65}   P>|z|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
contract_workers_abroad_2018_pc {c |}{col 33}{res}{space 2} .4658409{col 45}{space 2} .2939769{col 56}{space 1}    1.58{col 65}{space 3}0.113{col 73}{space 4}-.1103432{col 86}{space 3} 1.042025
{txt}{space 1}labor_services_workers_2018_pc {c |}{col 33}{res}{space 2} .1614635{col 45}{space 2} .1617873{col 56}{space 1}    1.00{col 65}{space 3}0.318{col 73}{space 4}-.1556338{col 86}{space 3} .4785608
{txt}{space 6}intl_student_pc_china2018 {c |}{col 33}{res}{space 2}  .159624{col 45}{space 2} .2655497{col 56}{space 1}    0.60{col 65}{space 3}0.548{col 73}{space 4}-.3608437{col 86}{space 3} .6800918
{txt}{space 4}migration_from_china_pc2019 {c |}{col 33}{res}{space 2} .0334477{col 45}{space 2} .0543155{col 56}{space 1}    0.62{col 65}{space 3}0.538{col 73}{space 4}-.0730087{col 86}{space 3} .1399042
{txt}{space 12}migration_total2019 {c |}{col 33}{res}{space 2}-.0007315{col 45}{space 2} .0112036{col 56}{space 1}   -0.07{col 65}{space 3}0.948{col 73}{space 4}-.0226902{col 86}{space 3} .0212272
{txt}{space 24}log_gdp {c |}{col 33}{res}{space 2} .0451596{col 45}{space 2} .0994021{col 56}{space 1}    0.45{col 65}{space 3}0.650{col 73}{space 4} -.149665{col 86}{space 3} .2399842
{txt}{space 21}log_gdp_pc {c |}{col 33}{res}{space 2}-.0729213{col 45}{space 2} .1796131{col 56}{space 1}   -0.41{col 65}{space 3}0.685{col 73}{space 4}-.4249566{col 86}{space 3} .2791139
{txt}{space 16}log_resource_pc {c |}{col 33}{res}{space 2}  .122425{col 45}{space 2} .0744073{col 56}{space 1}    1.65{col 65}{space 3}0.100{col 73}{space 4}-.0234107{col 86}{space 3} .2682608
{txt}{space 24}polity2 {c |}{col 33}{res}{space 2}-.0183847{col 45}{space 2} .0301128{col 56}{space 1}   -0.61{col 65}{space 3}0.542{col 73}{space 4}-.0774047{col 86}{space 3} .0406352
{txt}{space 20}health_2018 {c |}{col 33}{res}{space 2}-.0191653{col 45}{space 2} .0715799{col 56}{space 1}   -0.27{col 65}{space 3}0.789{col 73}{space 4}-.1594594{col 86}{space 3} .1211287
{txt}{space 18}distcap_china {c |}{col 33}{res}{space 2}-.0656164{col 45}{space 2}  .041973{col 56}{space 1}   -1.56{col 65}{space 3}0.118{col 73}{space 4} -.147882{col 86}{space 3} .0166492
{txt}{space 16}deaths_7_day_pc {c |}{col 33}{res}{space 2}-12.12575{col 45}{space 2} 10.96437{col 56}{space 1}   -1.11{col 65}{space 3}0.269{col 73}{space 4}-33.61552{col 86}{space 3} 9.364023
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{com}. eststo: stcox contract_workers_abroad_2018_pc labor_services_workers_2018_pc intl_student_pc_china2018 migration_from_china_pc2019 migration_total2019 log_gdp log_gdp_pc log_resource_pc polity2 health_2018 distcap_china fdi_china_inward_pc_2018 trade_china_gdp_2018 deaths_7_day_pc, nohr vce(robust)

         {txt}failure _d:  {res}be_any_1_China_carryover
   {txt}analysis time _t:  {res}date
                 {txt}id:  {res}cowcode

{txt}Iteration 0:   log pseudolikelihood = {res}-250.33727
{txt}Iteration 1:   log pseudolikelihood = {res} -249.6294
{txt}Iteration 2:   log pseudolikelihood = {res}-245.42067
{txt}Iteration 3:   log pseudolikelihood = {res} -241.8039
{txt}Iteration 4:   log pseudolikelihood = {res}-237.80969
{txt}Iteration 5:   log pseudolikelihood = {res}  -236.918
{txt}Iteration 6:   log pseudolikelihood = {res}-236.49162
{txt}Iteration 7:   log pseudolikelihood = {res}-236.42644
{txt}Iteration 8:   log pseudolikelihood = {res}-236.42379
{txt}Iteration 9:   log pseudolikelihood = {res}-236.42379
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res}-236.42379

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         122             {txt}Number of obs    =  {res}     6,831
{txt}No. of failures      = {res}          55
{txt}Time at risk         = {res}     2682047
                                                {txt}Wald chi2({res}14{txt})    =  {res}     72.42
{txt}Log pseudolikelihood =   {res}-236.42379             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 97:(Std. Err. adjusted for {res:122} clusters in cowcode)}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                             _t{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      z{col 65}   P>|z|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
contract_workers_abroad_2018_pc {c |}{col 33}{res}{space 2} .4026578{col 45}{space 2} .2863284{col 56}{space 1}    1.41{col 65}{space 3}0.160{col 73}{space 4}-.1585355{col 86}{space 3} .9638511
{txt}{space 1}labor_services_workers_2018_pc {c |}{col 33}{res}{space 2}  .307436{col 45}{space 2} .2627874{col 56}{space 1}    1.17{col 65}{space 3}0.242{col 73}{space 4}-.2076179{col 86}{space 3} .8224899
{txt}{space 6}intl_student_pc_china2018 {c |}{col 33}{res}{space 2} .4000022{col 45}{space 2} .4109087{col 56}{space 1}    0.97{col 65}{space 3}0.330{col 73}{space 4}-.4053641{col 86}{space 3} 1.205368
{txt}{space 4}migration_from_china_pc2019 {c |}{col 33}{res}{space 2}-.0089235{col 45}{space 2} .0893913{col 56}{space 1}   -0.10{col 65}{space 3}0.920{col 73}{space 4}-.1841273{col 86}{space 3} .1662802
{txt}{space 12}migration_total2019 {c |}{col 33}{res}{space 2} .0075804{col 45}{space 2} .0121834{col 56}{space 1}    0.62{col 65}{space 3}0.534{col 73}{space 4}-.0162986{col 86}{space 3} .0314594
{txt}{space 24}log_gdp {c |}{col 33}{res}{space 2} .1737147{col 45}{space 2} .1082195{col 56}{space 1}    1.61{col 65}{space 3}0.108{col 73}{space 4}-.0383916{col 86}{space 3}  .385821
{txt}{space 21}log_gdp_pc {c |}{col 33}{res}{space 2}-.2034799{col 45}{space 2} .1973978{col 56}{space 1}   -1.03{col 65}{space 3}0.303{col 73}{space 4}-.5903725{col 86}{space 3} .1834127
{txt}{space 16}log_resource_pc {c |}{col 33}{res}{space 2} .0546732{col 45}{space 2} .0887874{col 56}{space 1}    0.62{col 65}{space 3}0.538{col 73}{space 4}-.1193469{col 86}{space 3} .2286933
{txt}{space 24}polity2 {c |}{col 33}{res}{space 2}-.0288654{col 45}{space 2}   .03733{col 56}{space 1}   -0.77{col 65}{space 3}0.439{col 73}{space 4}-.1020307{col 86}{space 3}    .0443
{txt}{space 20}health_2018 {c |}{col 33}{res}{space 2}-.0076975{col 45}{space 2} .0824688{col 56}{space 1}   -0.09{col 65}{space 3}0.926{col 73}{space 4}-.1693333{col 86}{space 3} .1539383
{txt}{space 18}distcap_china {c |}{col 33}{res}{space 2}-.0317706{col 45}{space 2} .0460413{col 56}{space 1}   -0.69{col 65}{space 3}0.490{col 73}{space 4}-.1220098{col 86}{space 3} .0584687
{txt}{space 7}fdi_china_inward_pc_2018 {c |}{col 33}{res}{space 2}-.0000597{col 45}{space 2} .0000628{col 56}{space 1}   -0.95{col 65}{space 3}0.342{col 73}{space 4}-.0001828{col 86}{space 3} .0000634
{txt}{space 11}trade_china_gdp_2018 {c |}{col 33}{res}{space 2} .0023992{col 45}{space 2} .0153666{col 56}{space 1}    0.16{col 65}{space 3}0.876{col 73}{space 4}-.0277189{col 86}{space 3} .0325172
{txt}{space 16}deaths_7_day_pc {c |}{col 33}{res}{space 2}-12.19306{col 45}{space 2} 10.91841{col 56}{space 1}   -1.12{col 65}{space 3}0.264{col 73}{space 4}-33.59276{col 86}{space 3} 9.206632
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{com}. eststo: stcox contract_workers_abroad_2018_pc labor_services_workers_2018_pc intl_student_pc_china2018 migration_from_china_pc2019 migration_total2019 log_gdp log_gdp_pc log_resource_pc polity2 health_2018 distcap_china fdi_china_inward_pc_2018 trade_china_gdp_2018 trade_nonchina_gdp_2018 deaths_7_day_pc, nohr vce(robust)

         {txt}failure _d:  {res}be_any_1_China_carryover
   {txt}analysis time _t:  {res}date
                 {txt}id:  {res}cowcode

{txt}Iteration 0:   log pseudolikelihood = {res}-250.33727
{txt}Iteration 1:   log pseudolikelihood = {res}-249.53555
{txt}Iteration 2:   log pseudolikelihood = {res}-245.16864
{txt}Iteration 3:   log pseudolikelihood = {res}-241.76272
{txt}Iteration 4:   log pseudolikelihood = {res}-237.54591
{txt}Iteration 5:   log pseudolikelihood = {res}-236.73756
{txt}Iteration 6:   log pseudolikelihood = {res}-235.99125
{txt}Iteration 7:   log pseudolikelihood = {res}-235.91976
{txt}Iteration 8:   log pseudolikelihood = {res}-235.91682
{txt}Iteration 9:   log pseudolikelihood = {res}-235.91682
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res}-235.91682

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         122             {txt}Number of obs    =  {res}     6,831
{txt}No. of failures      = {res}          55
{txt}Time at risk         = {res}     2682047
                                                {txt}Wald chi2({res}15{txt})    =  {res}     75.50
{txt}Log pseudolikelihood =   {res}-235.91682             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 97:(Std. Err. adjusted for {res:122} clusters in cowcode)}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                             _t{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      z{col 65}   P>|z|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
contract_workers_abroad_2018_pc {c |}{col 33}{res}{space 2} .3831463{col 45}{space 2} .2973221{col 56}{space 1}    1.29{col 65}{space 3}0.198{col 73}{space 4}-.1995944{col 86}{space 3} .9658869
{txt}{space 1}labor_services_workers_2018_pc {c |}{col 33}{res}{space 2}  .326748{col 45}{space 2} .2588588{col 56}{space 1}    1.26{col 65}{space 3}0.207{col 73}{space 4}-.1806059{col 86}{space 3} .8341019
{txt}{space 6}intl_student_pc_china2018 {c |}{col 33}{res}{space 2} .3404318{col 45}{space 2} .4114459{col 56}{space 1}    0.83{col 65}{space 3}0.408{col 73}{space 4}-.4659874{col 86}{space 3} 1.146851
{txt}{space 4}migration_from_china_pc2019 {c |}{col 33}{res}{space 2}-.0066624{col 45}{space 2} .0880758{col 56}{space 1}   -0.08{col 65}{space 3}0.940{col 73}{space 4}-.1792878{col 86}{space 3}  .165963
{txt}{space 12}migration_total2019 {c |}{col 33}{res}{space 2} .0072333{col 45}{space 2} .0131744{col 56}{space 1}    0.55{col 65}{space 3}0.583{col 73}{space 4} -.018588{col 86}{space 3} .0330546
{txt}{space 24}log_gdp {c |}{col 33}{res}{space 2} .1288514{col 45}{space 2} .1193531{col 56}{space 1}    1.08{col 65}{space 3}0.280{col 73}{space 4}-.1050765{col 86}{space 3} .3627792
{txt}{space 21}log_gdp_pc {c |}{col 33}{res}{space 2} -.122077{col 45}{space 2} .2107197{col 56}{space 1}   -0.58{col 65}{space 3}0.562{col 73}{space 4}  -.53508{col 86}{space 3}  .290926
{txt}{space 16}log_resource_pc {c |}{col 33}{res}{space 2} .0486355{col 45}{space 2} .0848618{col 56}{space 1}    0.57{col 65}{space 3}0.567{col 73}{space 4}-.1176906{col 86}{space 3} .2149617
{txt}{space 24}polity2 {c |}{col 33}{res}{space 2}-.0311974{col 45}{space 2} .0360836{col 56}{space 1}   -0.86{col 65}{space 3}0.387{col 73}{space 4}-.1019201{col 86}{space 3} .0395252
{txt}{space 20}health_2018 {c |}{col 33}{res}{space 2}-.0051453{col 45}{space 2} .0800672{col 56}{space 1}   -0.06{col 65}{space 3}0.949{col 73}{space 4}-.1620741{col 86}{space 3} .1517836
{txt}{space 18}distcap_china {c |}{col 33}{res}{space 2}-.0436659{col 45}{space 2} .0456711{col 56}{space 1}   -0.96{col 65}{space 3}0.339{col 73}{space 4}-.1331796{col 86}{space 3} .0458477
{txt}{space 7}fdi_china_inward_pc_2018 {c |}{col 33}{res}{space 2}-.0000635{col 45}{space 2} .0000592{col 56}{space 1}   -1.07{col 65}{space 3}0.284{col 73}{space 4}-.0001796{col 86}{space 3} .0000526
{txt}{space 11}trade_china_gdp_2018 {c |}{col 33}{res}{space 2} .0060301{col 45}{space 2} .0154521{col 56}{space 1}    0.39{col 65}{space 3}0.696{col 73}{space 4}-.0242554{col 86}{space 3} .0363156
{txt}{space 8}trade_nonchina_gdp_2018 {c |}{col 33}{res}{space 2}-.0054083{col 45}{space 2} .0053393{col 56}{space 1}   -1.01{col 65}{space 3}0.311{col 73}{space 4}-.0158732{col 86}{space 3} .0050567
{txt}{space 16}deaths_7_day_pc {c |}{col 33}{res}{space 2}-12.80705{col 45}{space 2}  11.2802{col 56}{space 1}   -1.14{col 65}{space 3}0.256{col 73}{space 4}-34.91583{col 86}{space 3} 9.301726
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est6{txt} stored)

{com}. 
. esttab using "Table2.tex", label keep(contract_workers_abroad_2018_pc labor_services_workers_2018_pc intl_student_pc_china2018 migration_from_china_pc2019 migration_total2019 fdi_china_inward_pc_2018 log_gdp log_gdp_pc trade_china_gdp_2018 trade_nonchina_gdp_2018 log_resource_pc polity2 health_2018 distcap_china deaths_7_day_pc) order(contract_workers_abroad_2018_pc labor_services_workers_2018_pc intl_student_pc_china2018 migration_from_china_pc2019 migration_total2019 fdi_china_inward_pc_2018 log_gdp log_gdp_pc trade_china_gdp_2018 trade_nonchina_gdp_2018 log_resource_pc polity2 health_2018 distcap_china deaths_7_day_pc) nonotes se(3) b(3) replace star(+ 0.10 * 0.05 ** 0.01) compress nogaps nodepvars mtitles("" "" "" "" "" "") 
{res}{txt}(note: file Table2.tex not found)
(output written to {browse  `"Table2.tex"'})

{com}. 
. * Figure 3: Targeted Entry Restrictions against China by Date
. 
. drop if date > 22000 | date < 21930
{txt}(390 observations deleted)

{com}. replace be_cat_1_China = 1 if bf_cat_1_China == 1 & bna_cat_1_China == 1
{txt}(0 real changes made)

{com}. collapse(sum) be_cat_1_China be_cat_2_China be_cat_3_China, by(date)
{txt}
{com}. gen China_1 = be_cat_1_China
{txt}
{com}. gen China_1_2 = be_cat_1_China + be_cat_2_China
{txt}
{com}. gen China_1_3 = be_cat_1_China + be_cat_2_China + be_cat_3_China
{txt}
{com}. 
. twoway bar China_1 date, bcolor("127 0 0") || rbar China_1 China_1_2  date, bcolor("239 101 72") || rbar China_1_2 China_1_3 date, bcolor("253 187 132")||,   ytitle("Countries Introducing Restrictions", size(4))  yscale(titlegap(*5)) xscale(titlegap(*5)) xtitle("Date of Implementation", size(4))  legend (region(lcolor(white)) cols(3) lab(1 "Catogery 1") lab(2 "Category 2") lab(3 "Category 3") size(3)) plotregion(fcolor(white) lstyle(none) lcolor(white)) graphregion(fcolor(white) lstyle(none) lcolor(white)) title("Targeted Entry Restrictions against China by Date", color(black) size(5)) 
{res}{txt}
{com}. graph export "Figure3.pdf", replace
{txt}(file Figure3.pdf written in PDF format)

{com}. 
. * Figure 1: Overseas Chinese Workers per 10,000 Inhabitants (2018)
. 
. * ssc install spmap
. 
. use "Chinese_workers_map.dta", clear
{txt}
{com}. 
. spmap  LS2018pc using worldcoor3.dta if countryname!="Antarctica", id(id) fcolor(Blues2) clmethod(custom) clbreaks(0 0.5 1 2  5  614)  ocolor(white ..) osize(thin ..) legend(position(7) symy(*2) symx(*2) size(7)) legorder(lohi) title("", size(10)) saving(Figure1_a, replace)
{res}{txt}(note: file Figure1_a.gph not found)
{res}{txt}(file Figure1_a.gph saved)

{com}. graph export "Figure1_a.pdf", replace
{txt}(file Figure1_a.pdf written in PDF format)

{com}. 
. spmap  CP2018pc using worldcoor3.dta if countryname!="Antarctica", id(id) fcolor(Blues2) clmethod(custom) clbreaks(0 0.5 1 2  5  614)  ocolor(white ..) osize(thin ..) legend(position(7) symy(*2) symx(*2) size(7)) legorder(lohi) title("", size(10)) saving(Figure1_b, replace)
{res}{txt}(note: file Figure1_b.gph not found)
{res}{txt}(file Figure1_b.gph saved)

{com}. graph export "Figure1_b.pdf", replace
{txt}(file Figure1_b.pdf written in PDF format)

{com}. 
. ********************
. ***** APPENDiX *****
. ********************
. 
. use "COVID19_TRAC.dta", clear
{txt}
{com}. 
. * Table A1: Diagnostic Tests for the Proportionality Assumption
. 
. stset date, id (cowcode) failure (be_cat_1_China_carryover)

                {txt}id:  {res}cowcode
     {txt}failure event:  {res}be_cat_1_China_carryover != 0 & be_cat_1_China_carryover < .
{txt}obs. time interval:  {res}(date[_n-1], date]
{txt} exit on or before:  {res}failure

{txt}{hline 78}
{res}     14,235{txt}  total observations
{res}      2,774{txt}  observations begin on or after (first) failure
{hline 78}
{res}     11,461{txt}  observations remaining, representing
{res}        195{txt}  subjects
{res}         76{txt}  failures in single-failure-per-subject data
{res}  4,287,421{txt}  total analysis time at risk and under observation
                                                at risk from t = {res}        0
                                     {txt}earliest observed entry t = {res}        0
                                          {txt}last observed exit t = {res}   22,001
{txt}
{com}. stsum

         {txt}failure _d:  {res}be_cat_1_China_carryover
   {txt}analysis time _t:  {res}date
                 {txt}id:  {res}cowcode

{txt}{col 10}{c |}{col 26}incidence{col 42}no. of{col 52}{c LT}{hline 6} Survival time {hline 5}{c RT}
{col 10}{c |} time at risk{col 29}rate{col 41}subjects{col 57}25%{col 67}50%{col 77}75%
{hline 9}{c +}{hline 69}
   total {c |} {res}     4287421   .0000177{col 40}      195      21974         .         .
{txt}
{com}. 
. stcox contract_workers_abroad_2018_pc labor_services_workers_2018_pc intl_student_pc_china2018 migration_from_china_pc2019 migration_total2019 log_gdp log_gdp_pc log_resource_pc polity2 health_2018 distcap_china trade_china_gdp_2018 trade_nonchina_gdp_2018 deaths_7_day_pc, nohr vce(robust)

         {txt}failure _d:  {res}be_cat_1_China_carryover
   {txt}analysis time _t:  {res}date
                 {txt}id:  {res}cowcode

{txt}Iteration 0:   log pseudolikelihood = {res}-207.18337
{txt}Iteration 1:   log pseudolikelihood = {res} -204.3015
{txt}Iteration 2:   log pseudolikelihood = {res}-203.05877
{txt}Iteration 3:   log pseudolikelihood = {res}-197.37036
{txt}Iteration 4:   log pseudolikelihood = {res}-194.59944
{txt}Iteration 5:   log pseudolikelihood = {res}-192.64551
{txt}Iteration 6:   log pseudolikelihood = {res}-191.78919
{txt}Iteration 7:   log pseudolikelihood = {res}-191.06503
{txt}Iteration 8:   log pseudolikelihood = {res} -190.3857
{txt}Iteration 9:   log pseudolikelihood = {res}-190.10368
{txt}Iteration 10:  log pseudolikelihood = {res}-190.06282
{txt}Iteration 11:  log pseudolikelihood = {res}-190.06182
{txt}Iteration 12:  log pseudolikelihood = {res}-190.06182
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res}-190.06182

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         122             {txt}Number of obs    =  {res}     7,402
{txt}No. of failures      = {res}          45
{txt}Time at risk         = {res}     2682618
                                                {txt}Wald chi2({res}14{txt})    =  {res}     57.77
{txt}Log pseudolikelihood =   {res}-190.06182             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 97:(Std. Err. adjusted for {res:122} clusters in cowcode)}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                             _t{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      z{col 65}   P>|z|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
contract_workers_abroad_2018_pc {c |}{col 33}{res}{space 2} .8764252{col 45}{space 2} .3432111{col 56}{space 1}    2.55{col 65}{space 3}0.011{col 73}{space 4} .2037438{col 86}{space 3} 1.549107
{txt}{space 1}labor_services_workers_2018_pc {c |}{col 33}{res}{space 2} .3934546{col 45}{space 2}  .288369{col 56}{space 1}    1.36{col 65}{space 3}0.172{col 73}{space 4}-.1717382{col 86}{space 3} .9586474
{txt}{space 6}intl_student_pc_china2018 {c |}{col 33}{res}{space 2} .5172592{col 45}{space 2} .4524433{col 56}{space 1}    1.14{col 65}{space 3}0.253{col 73}{space 4}-.3695133{col 86}{space 3} 1.404032
{txt}{space 4}migration_from_china_pc2019 {c |}{col 33}{res}{space 2}-.0126755{col 45}{space 2} .0951656{col 56}{space 1}   -0.13{col 65}{space 3}0.894{col 73}{space 4}-.1991967{col 86}{space 3} .1738458
{txt}{space 12}migration_total2019 {c |}{col 33}{res}{space 2}-.0093478{col 45}{space 2} .0149955{col 56}{space 1}   -0.62{col 65}{space 3}0.533{col 73}{space 4}-.0387385{col 86}{space 3} .0200428
{txt}{space 24}log_gdp {c |}{col 33}{res}{space 2}-.0886194{col 45}{space 2} .1360173{col 56}{space 1}   -0.65{col 65}{space 3}0.515{col 73}{space 4}-.3552085{col 86}{space 3} .1779697
{txt}{space 21}log_gdp_pc {c |}{col 33}{res}{space 2} .1412945{col 45}{space 2} .2400524{col 56}{space 1}    0.59{col 65}{space 3}0.556{col 73}{space 4}-.3291994{col 86}{space 3} .6117885
{txt}{space 16}log_resource_pc {c |}{col 33}{res}{space 2} .0691206{col 45}{space 2} .0863638{col 56}{space 1}    0.80{col 65}{space 3}0.424{col 73}{space 4}-.1001494{col 86}{space 3} .2383905
{txt}{space 24}polity2 {c |}{col 33}{res}{space 2}-.0310488{col 45}{space 2} .0360459{col 56}{space 1}   -0.86{col 65}{space 3}0.389{col 73}{space 4}-.1016974{col 86}{space 3} .0395998
{txt}{space 20}health_2018 {c |}{col 33}{res}{space 2} .0404112{col 45}{space 2} .1016684{col 56}{space 1}    0.40{col 65}{space 3}0.691{col 73}{space 4}-.1588553{col 86}{space 3} .2396776
{txt}{space 18}distcap_china {c |}{col 33}{res}{space 2}-.0963451{col 45}{space 2} .0455876{col 56}{space 1}   -2.11{col 65}{space 3}0.035{col 73}{space 4}-.1856952{col 86}{space 3}-.0069951
{txt}{space 11}trade_china_gdp_2018 {c |}{col 33}{res}{space 2} .0133421{col 45}{space 2} .0139195{col 56}{space 1}    0.96{col 65}{space 3}0.338{col 73}{space 4}-.0139396{col 86}{space 3} .0406238
{txt}{space 8}trade_nonchina_gdp_2018 {c |}{col 33}{res}{space 2}-.0105432{col 45}{space 2} .0066626{col 56}{space 1}   -1.58{col 65}{space 3}0.114{col 73}{space 4}-.0236017{col 86}{space 3} .0025154
{txt}{space 16}deaths_7_day_pc {c |}{col 33}{res}{space 2}-27.33398{col 45}{space 2} 17.96909{col 56}{space 1}   -1.52{col 65}{space 3}0.128{col 73}{space 4}-62.55275{col 86}{space 3} 7.884794
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. stphtest, log detail

{txt}      Test of proportional-hazards assumption

      Time:  {res}Log(t)
{txt}      {hline 12}{c TT}{hline 51}
                  {c |}       rho            chi2       df       Prob>chi2
      {hline 12}{c +}{hline 51}
      contract_w~c{col 19}{c |}{res}{col 25}-0.09913{col 37}     0.56{col 50}    1{col 64}0.4539
{txt}      labor_serv~c{col 19}{c |}{res}{col 25} 0.14567{col 37}     1.01{col 50}    1{col 64}0.3146
{txt}      intl_st~2018{col 19}{c |}{res}{col 25} 0.11533{col 37}     0.77{col 50}    1{col 64}0.3798
{txt}      migrat~c2019{col 19}{c |}{res}{col 25}-0.14895{col 37}     1.24{col 50}    1{col 64}0.2650
{txt}      migrat~l2019{col 19}{c |}{res}{col 25} 0.04403{col 37}     0.16{col 50}    1{col 64}0.6937
{txt}      log_gdp{col 19}{c |}{res}{col 25} 0.12134{col 37}     1.59{col 50}    1{col 64}0.2069
{txt}      log_gdp_pc{col 19}{c |}{res}{col 25}-0.05827{col 37}     0.37{col 50}    1{col 64}0.5412
{txt}      log_resour~c{col 19}{c |}{res}{col 25} 0.11457{col 37}     0.75{col 50}    1{col 64}0.3853
{txt}      polity2{col 19}{c |}{res}{col 25}-0.01750{col 37}     0.03{col 50}    1{col 64}0.8707
{txt}      health_2018{col 19}{c |}{res}{col 25} 0.05754{col 37}     0.52{col 50}    1{col 64}0.4707
{txt}      distcap_ch~a{col 19}{c |}{res}{col 25} 0.13272{col 37}     0.96{col 50}    1{col 64}0.3284
{txt}      trade_c~2018{col 19}{c |}{res}{col 25}-0.02021{col 37}     0.02{col 50}    1{col 64}0.8899
{txt}      trade_n~2018{col 19}{c |}{res}{col 25} 0.18233{col 37}     2.92{col 50}    1{col 64}0.0875
{txt}      deaths_7_d~c{col 19}{c |}{res}{col 25} 0.12697{col 37}     0.49{col 50}    1{col 64}0.4843
{txt}      {hline 12}{c +}{hline 51}
      global test {c |}{res}{col 37}     8.84{col 50}   14{col 64}0.8412
{txt}      {hline 12}{c BT}{hline 51}

note: robust variance-covariance matrix used.

{com}. 
. * Table A2: Category 1 Entry Restrictions (Interaction with Time Added)
. 
. gen time_trade_non_china = time * trade_nonchina_gdp_2018
{txt}(3,723 missing values generated)

{com}. label var time_trade_non_china "Time * Trade with Others"
{txt}
{com}. 
. eststo clear
{txt}
{com}. 
. eststo: stcox contract_workers_abroad_2018_pc labor_services_workers_2018_pc intl_student_pc_china2018 migration_from_china_pc2019 migration_total2019 log_gdp log_gdp_pc log_resource_pc polity2 health_2018 distcap_china fdi_china_inward_pc_2018 trade_china_gdp_2018 trade_nonchina_gdp_2018 time_trade_non_china deaths_7_day_pc, nohr vce(robust)

         {txt}failure _d:  {res}be_cat_1_China_carryover
   {txt}analysis time _t:  {res}date
                 {txt}id:  {res}cowcode

{txt}Iteration 0:   log pseudolikelihood = {res}-207.18337
{txt}Iteration 1:   log pseudolikelihood = {res}-203.94218
{txt}Iteration 2:   log pseudolikelihood = {res} -202.9909
{txt}Iteration 3:   log pseudolikelihood = {res}-197.36359
{txt}Iteration 4:   log pseudolikelihood = {res}-193.70348
{txt}Iteration 5:   log pseudolikelihood = {res}-190.60735
{txt}Iteration 6:   log pseudolikelihood = {res}-189.93382
{txt}Iteration 7:   log pseudolikelihood = {res} -189.2029
{txt}Iteration 8:   log pseudolikelihood = {res} -188.5314
{txt}Iteration 9:   log pseudolikelihood = {res}-188.29584
{txt}Iteration 10:  log pseudolikelihood = {res}-188.27027
{txt}Iteration 11:  log pseudolikelihood = {res} -188.2699
{txt}Iteration 12:  log pseudolikelihood = {res} -188.2699
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res} -188.2699

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         122             {txt}Number of obs    =  {res}     7,402
{txt}No. of failures      = {res}          45
{txt}Time at risk         = {res}     2682618
                                                {txt}Wald chi2({res}16{txt})    =  {res}     69.52
{txt}Log pseudolikelihood =   {res} -188.2699             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 97:(Std. Err. adjusted for {res:122} clusters in cowcode)}
{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                             _t{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      z{col 65}   P>|z|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
contract_workers_abroad_2018_pc {c |}{col 33}{res}{space 2} .9518763{col 45}{space 2} .3334334{col 56}{space 1}    2.85{col 65}{space 3}0.004{col 73}{space 4} .2983588{col 86}{space 3} 1.605394
{txt}{space 1}labor_services_workers_2018_pc {c |}{col 33}{res}{space 2} .5193172{col 45}{space 2} .3045808{col 56}{space 1}    1.71{col 65}{space 3}0.088{col 73}{space 4}-.0776501{col 86}{space 3} 1.116285
{txt}{space 6}intl_student_pc_china2018 {c |}{col 33}{res}{space 2} .3375051{col 45}{space 2} .4716534{col 56}{space 1}    0.72{col 65}{space 3}0.474{col 73}{space 4}-.5869185{col 86}{space 3} 1.261929
{txt}{space 4}migration_from_china_pc2019 {c |}{col 33}{res}{space 2} .0198088{col 45}{space 2} .0980481{col 56}{space 1}    0.20{col 65}{space 3}0.840{col 73}{space 4} -.172362{col 86}{space 3} .2119796
{txt}{space 12}migration_total2019 {c |}{col 33}{res}{space 2}-.0086618{col 45}{space 2} .0144262{col 56}{space 1}   -0.60{col 65}{space 3}0.548{col 73}{space 4}-.0369367{col 86}{space 3}  .019613
{txt}{space 24}log_gdp {c |}{col 33}{res}{space 2}-.0947333{col 45}{space 2} .1374072{col 56}{space 1}   -0.69{col 65}{space 3}0.491{col 73}{space 4}-.3640464{col 86}{space 3} .1745798
{txt}{space 21}log_gdp_pc {c |}{col 33}{res}{space 2} .1376602{col 45}{space 2} .2405532{col 56}{space 1}    0.57{col 65}{space 3}0.567{col 73}{space 4}-.3338154{col 86}{space 3} .6091358
{txt}{space 16}log_resource_pc {c |}{col 33}{res}{space 2}  .069158{col 45}{space 2} .0892262{col 56}{space 1}    0.78{col 65}{space 3}0.438{col 73}{space 4}-.1057221{col 86}{space 3} .2440381
{txt}{space 24}polity2 {c |}{col 33}{res}{space 2}-.0305846{col 45}{space 2} .0352575{col 56}{space 1}   -0.87{col 65}{space 3}0.386{col 73}{space 4} -.099688{col 86}{space 3} .0385189
{txt}{space 20}health_2018 {c |}{col 33}{res}{space 2} .0305137{col 45}{space 2} .0996412{col 56}{space 1}    0.31{col 65}{space 3}0.759{col 73}{space 4}-.1647795{col 86}{space 3} .2258068
{txt}{space 18}distcap_china {c |}{col 33}{res}{space 2}-.0930651{col 45}{space 2}  .044372{col 56}{space 1}   -2.10{col 65}{space 3}0.036{col 73}{space 4}-.1800326{col 86}{space 3}-.0060976
{txt}{space 7}fdi_china_inward_pc_2018 {c |}{col 33}{res}{space 2} -.000173{col 45}{space 2} .0002905{col 56}{space 1}   -0.60{col 65}{space 3}0.551{col 73}{space 4}-.0007424{col 86}{space 3} .0003963
{txt}{space 11}trade_china_gdp_2018 {c |}{col 33}{res}{space 2} .0119409{col 45}{space 2} .0141503{col 56}{space 1}    0.84{col 65}{space 3}0.399{col 73}{space 4}-.0157933{col 86}{space 3}  .039675
{txt}{space 8}trade_nonchina_gdp_2018 {c |}{col 33}{res}{space 2}-.0345697{col 45}{space 2}  .014757{col 56}{space 1}   -2.34{col 65}{space 3}0.019{col 73}{space 4}-.0634929{col 86}{space 3}-.0056464
{txt}{space 11}time_trade_non_china {c |}{col 33}{res}{space 2} .0005394{col 45}{space 2}  .000297{col 56}{space 1}    1.82{col 65}{space 3}0.069{col 73}{space 4}-.0000426{col 86}{space 3} .0011214
{txt}{space 16}deaths_7_day_pc {c |}{col 33}{res}{space 2}-27.66456{col 45}{space 2} 17.74411{col 56}{space 1}   -1.56{col 65}{space 3}0.119{col 73}{space 4}-62.44236{col 86}{space 3}  7.11325
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}. 
. esttab using "TableA2.tex", label keep(contract_workers_abroad_2018_pc labor_services_workers_2018_pc intl_student_pc_china2018 migration_from_china_pc2019 migration_total2019 fdi_china_inward_pc_2018 log_gdp log_gdp_pc trade_china_gdp_2018 trade_nonchina_gdp_2018 log_resource_pc polity2 health_2018 distcap_china time_trade_non_china deaths_7_day_pc) order(contract_workers_abroad_2018_pc labor_services_workers_2018_pc intl_student_pc_china2018 migration_from_china_pc2019 migration_total2019 fdi_china_inward_pc_2018 log_gdp log_gdp_pc trade_china_gdp_2018 trade_nonchina_gdp_2018 log_resource_pc polity2 health_2018 distcap_china time_trade_non_china deaths_7_day_pc) nonotes se(3) b(3) replace star(+ 0.10 * 0.05 ** 0.01) compress nogaps nodepvars mtitles("" "" "" "" "" "") 
{res}{txt}(note: file TableA2.tex not found)
(output written to {browse  `"TableA2.tex"'})

{com}. 
. * Table A3: Binary Probit: Category 1 Entry Restrictions
. 
. eststo clear 
{txt}
{com}. eststo: dprobit be_cat_1_China_carryover contract_workers_abroad_2018_pc migration_from_china_pc2019 migration_total2019 log_gdp log_gdp_pc log_resource_pc polity2 health_2018 distcap_china fdi_china_inward_pc_2018 trade_china_gdp_2018 trade_nonchina_gdp_2018 deaths_7_day_pc if date == 22001, robust

{txt}Iteration 0:   log pseudolikelihood = {res}-80.317738
{txt}Iteration 1:   log pseudolikelihood = {res}-69.682398
{txt}Iteration 2:   log pseudolikelihood = {res} -67.46405
{txt}Iteration 3:   log pseudolikelihood = {res}-65.611623
{txt}Iteration 4:   log pseudolikelihood = {res}-64.692997
{txt}Iteration 5:   log pseudolikelihood = {res}-64.255996
{txt}Iteration 6:   log pseudolikelihood = {res}-64.230471
{txt}Iteration 7:   log pseudolikelihood = {res}-64.230398
{txt}Iteration 8:   log pseudolikelihood = {res}-64.230398

{txt}Probit regression, reporting marginal effects           Number of obs ={res}    122
                                                        {txt}Wald chi2({res}13{txt}) ={res}  25.15
                                                        {txt}Prob > chi2   ={res} 0.0220
{txt}Log pseudolikelihood = {res}-64.230398                       {txt}Pseudo R2     ={res} 0.2003

{txt}{hline 9}{c TT}{hline 68}
{col 10}{c |}{col 26}Robust
be_cat.. {c |}{col 17}dF/dx{col 25}Std. Err.{col 40}z{col 45}P>|z|{col 55}x-bar{col 62}[    95% C.I.   ]
{hline 9}{c +}{hline 68}
contra~c {c |}  {res} .3569262   .2464909     1.86   0.063   .122126  -.126187   .84004
{txt}mi~c2019 {c |}  {res} .0039037   .0092235     0.45   0.653    1.7737  -.014174  .021981
{txt}mi~l2019 {c |}  {res}-.0003391    .003468    -0.10   0.922   9.91694  -.007136  .006458
 {txt}log_gdp {c |}  {res} .0018835   .0237893     0.08   0.937   25.2676  -.044743   .04851
{txt}log_gd~c {c |}  {res} .0484656   .0393154     1.14   0.255   8.81472  -.028591  .125522
{txt}log_re~c {c |}  {res}-.0027091   .0158885    -0.17   0.865   3.74735   -.03385  .028432
 {txt}polity2 {c |}  {res}-.0042261   .0070846    -0.62   0.533    4.9918  -.018112  .009659
{txt}hea~2018 {c |}  {res} .0226081   .0156285     1.60   0.109   6.71384  -.008023  .053239
{txt}distca~a {c |}  {res}-.0133107   .0091177    -1.57   0.116   8.85246  -.031181   .00456
{txt}fdi~2018 {c |}  {res} .0000262   .0000303     0.77   0.442    408.75  -.000033  .000086
{txt}trade_c~ {c |}  {res} .0017304   .0031317     0.57   0.568   7.69654  -.004408  .007868
{txt}t~nonc~8 {c |}  {res}-.0015156   .0011511    -1.40   0.163    60.294  -.003772   .00074
{txt}dea~y_pc {c |}  {res}-.5463212   .0806311    -2.58   0.010   .455098  -.704355 -.388287
{txt}{hline 9}{c +}{hline 68}
  obs. P {c |}  {res} .3688525
{txt} pred. P {c |}  {res} .1447631{txt}  (at x-bar)
{hline 9}{c BT}{hline 68}
{p 4 0 0}z and P>|z| correspond to the test of the underlying coefficient being 0
({res}est1{txt} stored)

{com}. eststo: dprobit be_cat_1_China_carryover labor_services_workers_2018_pc migration_from_china_pc2019 migration_total2019 log_gdp log_gdp_pc log_resource_pc polity2 health_2018 distcap_china fdi_china_inward_pc_2018 trade_china_gdp_2018 trade_nonchina_gdp_2018 deaths_7_day_pc if date == 22001, robust

{txt}Iteration 0:   log pseudolikelihood = {res}-80.317738
{txt}Iteration 1:   log pseudolikelihood = {res}-71.072307
{txt}Iteration 2:   log pseudolikelihood = {res}-69.367493
{txt}Iteration 3:   log pseudolikelihood = {res} -67.30642
{txt}Iteration 4:   log pseudolikelihood = {res}-65.814186
{txt}Iteration 5:   log pseudolikelihood = {res}  -65.1952
{txt}Iteration 6:   log pseudolikelihood = {res}-65.156009
{txt}Iteration 7:   log pseudolikelihood = {res} -65.14785
{txt}Iteration 8:   log pseudolikelihood = {res}-65.147728

{txt}Probit regression, reporting marginal effects           Number of obs ={res}    122
                                                        {txt}Wald chi2({res}13{txt}) ={res}  20.45
                                                        {txt}Prob > chi2   ={res} 0.0846
{txt}Log pseudolikelihood = {res}-65.147728                       {txt}Pseudo R2     ={res} 0.1889

{txt}{hline 9}{c TT}{hline 68}
{col 10}{c |}{col 26}Robust
be_cat.. {c |}{col 17}dF/dx{col 25}Std. Err.{col 40}z{col 45}P>|z|{col 55}x-bar{col 62}[    95% C.I.   ]
{hline 9}{c +}{hline 68}
labor_~c {c |}  {res} .4104193   .4066632     1.19   0.235    .20113  -.386626  1.20746
{txt}mi~c2019 {c |}  {res} .0028802   .0136822     0.21   0.834    1.7737  -.023936  .029697
{txt}mi~l2019 {c |}  {res} .0000516   .0038011     0.01   0.989   9.91694  -.007398  .007502
 {txt}log_gdp {c |}  {res}-.0090098   .0261429    -0.33   0.741   25.2676  -.060249  .042229
{txt}log_gd~c {c |}  {res} .0536577   .0448589     1.04   0.296   8.81472  -.034264   .14158
{txt}log_re~c {c |}  {res} .0084855   .0187789     0.47   0.641   3.74735   -.02832  .045291
 {txt}polity2 {c |}  {res}-.0050774   .0083525    -0.66   0.511    4.9918  -.021448  .011293
{txt}hea~2018 {c |}  {res} .0230531    .018715     1.45   0.146   6.71384  -.013628  .059734
{txt}distca~a {c |}  {res}-.0157105   .0103892    -1.54   0.123   8.85246  -.036073  .004652
{txt}fdi~2018 {c |}  {res}-.0000159   .0002456    -0.06   0.949    408.75  -.000497  .000466
{txt}trade_c~ {c |}  {res} .0014071    .003876     0.36   0.720   7.69654   -.00619  .009004
{txt}t~nonc~8 {c |}  {res}-.0015434   .0013369    -1.12   0.262    60.294  -.004164  .001077
{txt}dea~y_pc {c |}  {res}-.6322132    .167111    -2.45   0.014   .455098  -.959745 -.304682
{txt}{hline 9}{c +}{hline 68}
  obs. P {c |}  {res} .3688525
{txt} pred. P {c |}  {res}  .173587{txt}  (at x-bar)
{hline 9}{c BT}{hline 68}
{p 4 0 0}z and P>|z| correspond to the test of the underlying coefficient being 0
({res}est2{txt} stored)

{com}. eststo: dprobit be_cat_1_China_carryover intl_student_pc_china2018 migration_from_china_pc2019 migration_total2019 log_gdp log_gdp_pc log_resource_pc polity2 health_2018 distcap_china fdi_china_inward_pc_2018 trade_china_gdp_2018 trade_nonchina_gdp_2018 deaths_7_day_pc if date == 22001, robust

{txt}Iteration 0:   log pseudolikelihood = {res}-80.317738
{txt}Iteration 1:   log pseudolikelihood = {res}-71.200149
{txt}Iteration 2:   log pseudolikelihood = {res}-69.638118
{txt}Iteration 3:   log pseudolikelihood = {res}-67.644752
{txt}Iteration 4:   log pseudolikelihood = {res}-66.431815
{txt}Iteration 5:   log pseudolikelihood = {res} -65.91284
{txt}Iteration 6:   log pseudolikelihood = {res}-65.892259
{txt}Iteration 7:   log pseudolikelihood = {res} -65.89223

{txt}Probit regression, reporting marginal effects           Number of obs ={res}    122
                                                        {txt}Wald chi2({res}13{txt}) ={res}  26.02
                                                        {txt}Prob > chi2   ={res} 0.0169
{txt}Log pseudolikelihood = {res} -65.89223                       {txt}Pseudo R2     ={res} 0.1796

{txt}{hline 9}{c TT}{hline 68}
{col 10}{c |}{col 26}Robust
be_cat.. {c |}{col 17}dF/dx{col 25}Std. Err.{col 40}z{col 45}P>|z|{col 55}x-bar{col 62}[    95% C.I.   ]
{hline 9}{c +}{hline 68}
int~2018 {c |}  {res}-.0055709    .042231    -0.13   0.894   .166751  -.088342    .0772
{txt}mi~c2019 {c |}  {res} .0029256   .0090347     0.35   0.729    1.7737  -.014782  .020633
{txt}mi~l2019 {c |}  {res}  .001013   .0029118     0.36   0.721   9.91694  -.004694   .00672
 {txt}log_gdp {c |}  {res} -.009673   .0199858    -0.47   0.636   25.2676  -.048845  .029499
{txt}log_gd~c {c |}  {res} .0525796   .0366661     1.35   0.176   8.81472  -.019285  .124444
{txt}log_re~c {c |}  {res} .0035467    .014215     0.25   0.801   3.74735  -.024314  .031408
 {txt}polity2 {c |}  {res}-.0037277   .0063289    -0.62   0.534    4.9918  -.016132  .008677
{txt}hea~2018 {c |}  {res} .0165413   .0139442     1.34   0.180   6.71384  -.010789  .043871
{txt}distca~a {c |}  {res}-.0133243   .0088466    -1.75   0.081   8.85246  -.030663  .004015
{txt}fdi~2018 {c |}  {res} .0000314   .0000124     1.46   0.143    408.75   7.0e-06  .000056
{txt}trade_c~ {c |}  {res} .0022958    .003256     0.77   0.440   7.69654  -.004086  .008678
{txt}t~nonc~8 {c |}  {res}-.0015374   .0011345    -1.54   0.124    60.294  -.003761  .000686
{txt}dea~y_pc {c |}  {res}  -.53884   .0817681    -2.68   0.007   .455098  -.699103 -.378578
{txt}{hline 9}{c +}{hline 68}
  obs. P {c |}  {res} .3688525
{txt} pred. P {c |}  {res}  .123826{txt}  (at x-bar)
{hline 9}{c BT}{hline 68}
{p 4 0 0}z and P>|z| correspond to the test of the underlying coefficient being 0
({res}est3{txt} stored)

{com}. eststo: dprobit be_cat_1_China_carryover contract_workers_abroad_2018_pc labor_services_workers_2018_pc intl_student_pc_china2018 migration_from_china_pc2019 migration_total2019 log_gdp log_gdp_pc log_resource_pc polity2 health_2018 distcap_china fdi_china_inward_pc_2018 trade_china_gdp_2018 trade_nonchina_gdp_2018 deaths_7_day_pc if date == 22001, robust

{txt}Iteration 0:   log pseudolikelihood = {res}-80.317738
{txt}Iteration 1:   log pseudolikelihood = {res}-69.602159
{txt}Iteration 2:   log pseudolikelihood = {res}-67.288765
{txt}Iteration 3:   log pseudolikelihood = {res}-65.355395
{txt}Iteration 4:   log pseudolikelihood = {res}-64.445746
{txt}Iteration 5:   log pseudolikelihood = {res}-63.941162
{txt}Iteration 6:   log pseudolikelihood = {res}-63.876405
{txt}Iteration 7:   log pseudolikelihood = {res}-63.875654
{txt}Iteration 8:   log pseudolikelihood = {res}-63.875654

{txt}Probit regression, reporting marginal effects           Number of obs ={res}    122
                                                        {txt}Wald chi2({res}15{txt}) ={res}  29.27
                                                        {txt}Prob > chi2   ={res} 0.0148
{txt}Log pseudolikelihood = {res}-63.875654                       {txt}Pseudo R2     ={res} 0.2047

{txt}{hline 9}{c TT}{hline 68}
{col 10}{c |}{col 26}Robust
be_cat.. {c |}{col 17}dF/dx{col 25}Std. Err.{col 40}z{col 45}P>|z|{col 55}x-bar{col 62}[    95% C.I.   ]
{hline 9}{c +}{hline 68}
contra~c {c |}  {res} .3408328    .247422     1.73   0.083   .122126  -.144105  .825771
{txt}labor_~c {c |}  {res}  .197241   .2864725     0.76   0.450    .20113  -.364235  .758717
{txt}int~2018 {c |}  {res} .0837541   .1138973     0.72   0.471   .166751   -.13948  .306989
{txt}mi~c2019 {c |}  {res} -.005747   .0211583    -0.27   0.788    1.7737  -.047216  .035722
{txt}mi~l2019 {c |}  {res}-.0005685     .00331    -0.17   0.864   9.91694  -.007056  .005919
 {txt}log_gdp {c |}  {res}-.0012483    .022959    -0.05   0.957   25.2676  -.046247  .043751
{txt}log_gd~c {c |}  {res} .0457531   .0373415     1.08   0.280   8.81472  -.027435  .118941
{txt}log_re~c {c |}  {res} .0011385   .0158884     0.07   0.943   3.74735  -.030002  .032279
 {txt}polity2 {c |}  {res}-.0037153   .0070711    -0.57   0.570    4.9918  -.017574  .010144
{txt}hea~2018 {c |}  {res} .0224429   .0163458     1.65   0.099   6.71384  -.009594   .05448
{txt}distca~a {c |}  {res}-.0130763   .0087882    -1.56   0.120   8.85246  -.030301  .004148
{txt}fdi~2018 {c |}  {res}-.0001322   .0001939    -0.61   0.539    408.75  -.000512  .000248
{txt}trade_c~ {c |}  {res} .0015955   .0030854     0.50   0.616   7.69654  -.004452  .007643
{txt}t~nonc~8 {c |}  {res}-.0013622   .0011356    -1.19   0.236    60.294  -.003588  .000864
{txt}dea~y_pc {c |}  {res} -.487304   .1694045    -2.40   0.016   .455098  -.819331 -.155277
{txt}{hline 9}{c +}{hline 68}
  obs. P {c |}  {res} .3688525
{txt} pred. P {c |}  {res} .1332902{txt}  (at x-bar)
{hline 9}{c BT}{hline 68}
{p 4 0 0}z and P>|z| correspond to the test of the underlying coefficient being 0
({res}est4{txt} stored)

{com}. 
. esttab using "TableA3.tex", label keep(contract_workers_abroad_2018_pc labor_services_workers_2018_pc intl_student_pc_china2018 migration_from_china_pc2019 migration_total2019 fdi_china_inward_pc_2018 log_gdp log_gdp_pc trade_china_gdp_2018 trade_nonchina_gdp_2018 log_resource_pc polity2 health_2018 distcap_china deaths_7_day_pc) order(contract_workers_abroad_2018_pc labor_services_workers_2018_pc intl_student_pc_china2018 migration_from_china_pc2019 migration_total2019 fdi_china_inward_pc_2018 log_gdp log_gdp_pc trade_china_gdp_2018 trade_nonchina_gdp_2018 log_resource_pc polity2 health_2018 distcap_china deaths_7_day_pc) nonotes se(3) b(3) replace star(+ 0.10 * 0.05 ** 0.01) compress nogaps nodepvars mtitles("" "" "" "" "" "") 
{res}{txt}(note: file TableA3.tex not found)
(output written to {browse  `"TableA3.tex"'})

{com}. 
. * Table A4: Negative Binomial: Number of Days to Category 1 Entry Restrictions
. 
. eststo clear
{txt}
{com}. 
. eststo: nbreg days_to_category1 contract_workers_abroad_2018_pc migration_from_china_pc2019 migration_total2019 log_gdp log_gdp_pc log_resource_pc polity2 health_2018 distcap_china deaths_pc if date == 22001, vce(robust)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-923.44857}  
Iteration 1:{space 3}log pseudolikelihood = {res:-923.30174}  
Iteration 2:{space 3}log pseudolikelihood = {res:-923.30166}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-755.43727}  
Iteration 1:{space 3}log pseudolikelihood = {res:-714.76592}  
Iteration 2:{space 3}log pseudolikelihood = {res:-679.69272}  
Iteration 3:{space 3}log pseudolikelihood = {res:-679.68745}  
Iteration 4:{space 3}log pseudolikelihood = {res:-679.68745}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-665.12457}  
Iteration 1:{space 3}log pseudolikelihood = {res:-663.26459}  
Iteration 2:{space 3}log pseudolikelihood = {res:-663.23759}  
Iteration 3:{space 3}log pseudolikelihood = {res:-663.23758}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       148
{txt}{col 49}Wald chi2({res}10{txt}){col 67}= {res}     67.68
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-663.23758{txt}{col 49}Pseudo R2{col 67}= {res}    0.0242

{txt}{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}              days_to_category1{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      z{col 65}   P>|z|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
contract_workers_abroad_2018_pc {c |}{col 33}{res}{space 2}-.3624049{col 45}{space 2} .1090677{col 56}{space 1}   -3.32{col 65}{space 3}0.001{col 73}{space 4}-.5761737{col 86}{space 3}-.1486361
{txt}{space 4}migration_from_china_pc2019 {c |}{col 33}{res}{space 2}-.0298844{col 45}{space 2} .0070553{col 56}{space 1}   -4.24{col 65}{space 3}0.000{col 73}{space 4}-.0437125{col 86}{space 3}-.0160563
{txt}{space 12}migration_total2019 {c |}{col 33}{res}{space 2} .0009524{col 45}{space 2} .0025987{col 56}{space 1}    0.37{col 65}{space 3}0.714{col 73}{space 4}-.0041409{col 86}{space 3} .0060458
{txt}{space 24}log_gdp {c |}{col 33}{res}{space 2} .0131113{col 45}{space 2} .0223917{col 56}{space 1}    0.59{col 65}{space 3}0.558{col 73}{space 4}-.0307757{col 86}{space 3} .0569983
{txt}{space 21}log_gdp_pc {c |}{col 33}{res}{space 2} .0140687{col 45}{space 2} .0371391{col 56}{space 1}    0.38{col 65}{space 3}0.705{col 73}{space 4}-.0587226{col 86}{space 3}   .08686
{txt}{space 16}log_resource_pc {c |}{col 33}{res}{space 2}-.0189793{col 45}{space 2} .0157032{col 56}{space 1}   -1.21{col 65}{space 3}0.227{col 73}{space 4} -.049757{col 86}{space 3} .0117984
{txt}{space 24}polity2 {c |}{col 33}{res}{space 2}-.0058778{col 45}{space 2} .0066178{col 56}{space 1}   -0.89{col 65}{space 3}0.374{col 73}{space 4}-.0188485{col 86}{space 3} .0070929
{txt}{space 20}health_2018 {c |}{col 33}{res}{space 2}-.0005174{col 45}{space 2} .0172614{col 56}{space 1}   -0.03{col 65}{space 3}0.976{col 73}{space 4}-.0343491{col 86}{space 3} .0333142
{txt}{space 18}distcap_china {c |}{col 33}{res}{space 2} .0183465{col 45}{space 2}   .00906{col 56}{space 1}    2.03{col 65}{space 3}0.043{col 73}{space 4} .0005893{col 86}{space 3} .0361038
{txt}{space 22}deaths_pc {c |}{col 33}{res}{space 2} .0020649{col 45}{space 2} .0009115{col 56}{space 1}    2.27{col 65}{space 3}0.023{col 73}{space 4} .0002784{col 86}{space 3} .0038514
{txt}{space 26}_cons {c |}{col 33}{res}{space 2} 3.648978{col 45}{space 2} .4286181{col 56}{space 1}    8.51{col 65}{space 3}0.000{col 73}{space 4} 2.808901{col 86}{space 3} 4.489054
{txt}{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}/lnalpha {c |}{col 33}{res}{space 2} -2.11545{col 45}{space 2} .2076059{col 73}{space 4} -2.52235{col 86}{space 3} -1.70855
{txt}{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                          alpha {c |}{col 33}{res}{space 2}  .120579{col 45}{space 2} .0250329{col 73}{space 4} .0802707{col 86}{space 3} .1811282
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}. eststo: nbreg days_to_category1 labor_services_workers_2018_pc migration_from_china_pc2019 migration_total2019 log_gdp log_gdp_pc log_resource_pc polity2 health_2018 distcap_china deaths_pc if date == 22001, vce(robust)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-957.61406}  
Iteration 1:{space 3}log pseudolikelihood = {res:-957.34596}  
Iteration 2:{space 3}log pseudolikelihood = {res:-957.34526}  
Iteration 3:{space 3}log pseudolikelihood = {res:-957.34526}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-755.43727}  
Iteration 1:{space 3}log pseudolikelihood = {res:-714.76592}  
Iteration 2:{space 3}log pseudolikelihood = {res:-679.69272}  
Iteration 3:{space 3}log pseudolikelihood = {res:-679.68745}  
Iteration 4:{space 3}log pseudolikelihood = {res:-679.68745}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-668.86039}  
Iteration 1:{space 3}log pseudolikelihood = {res:-667.86145}  
Iteration 2:{space 3}log pseudolikelihood = {res:-667.85371}  
Iteration 3:{space 3}log pseudolikelihood = {res:-667.85371}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       148
{txt}{col 49}Wald chi2({res}10{txt}){col 67}= {res}    350.86
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-667.85371{txt}{col 49}Pseudo R2{col 67}= {res}    0.0174

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}    Robust
{col 1}             days_to_category1{col 32}{c |}      Coef.{col 44}   Std. Err.{col 56}      z{col 64}   P>|z|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
labor_services_workers_2018_pc {c |}{col 32}{res}{space 2}-.0162052{col 44}{space 2} .0368969{col 55}{space 1}   -0.44{col 64}{space 3}0.661{col 72}{space 4}-.0885219{col 85}{space 3} .0561114
{txt}{space 3}migration_from_china_pc2019 {c |}{col 32}{res}{space 2}-.0266404{col 44}{space 2} .0138515{col 55}{space 1}   -1.92{col 64}{space 3}0.054{col 72}{space 4}-.0537887{col 85}{space 3}  .000508
{txt}{space 11}migration_total2019 {c |}{col 32}{res}{space 2}-.0011029{col 44}{space 2} .0030234{col 55}{space 1}   -0.36{col 64}{space 3}0.715{col 72}{space 4}-.0070286{col 85}{space 3} .0048227
{txt}{space 23}log_gdp {c |}{col 32}{res}{space 2} .0254488{col 44}{space 2} .0242197{col 55}{space 1}    1.05{col 64}{space 3}0.293{col 72}{space 4}-.0220209{col 85}{space 3} .0729185
{txt}{space 20}log_gdp_pc {c |}{col 32}{res}{space 2} .0095432{col 44}{space 2} .0400597{col 55}{space 1}    0.24{col 64}{space 3}0.812{col 72}{space 4}-.0689724{col 85}{space 3} .0880588
{txt}{space 15}log_resource_pc {c |}{col 32}{res}{space 2} -.032433{col 44}{space 2} .0162905{col 55}{space 1}   -1.99{col 64}{space 3}0.046{col 72}{space 4}-.0643619{col 85}{space 3}-.0005042
{txt}{space 23}polity2 {c |}{col 32}{res}{space 2}-.0035667{col 44}{space 2} .0068381{col 55}{space 1}   -0.52{col 64}{space 3}0.602{col 72}{space 4} -.016969{col 85}{space 3} .0098356
{txt}{space 19}health_2018 {c |}{col 32}{res}{space 2} .0035978{col 44}{space 2} .0177836{col 55}{space 1}    0.20{col 64}{space 3}0.840{col 72}{space 4}-.0312574{col 85}{space 3} .0384531
{txt}{space 17}distcap_china {c |}{col 32}{res}{space 2} .0188183{col 44}{space 2} .0096363{col 55}{space 1}    1.95{col 64}{space 3}0.051{col 72}{space 4}-.0000686{col 85}{space 3} .0377051
{txt}{space 21}deaths_pc {c |}{col 32}{res}{space 2}  .001944{col 44}{space 2}  .001003{col 55}{space 1}    1.94{col 64}{space 3}0.053{col 72}{space 4}-.0000219{col 85}{space 3} .0039099
{txt}{space 25}_cons {c |}{col 32}{res}{space 2} 3.357439{col 44}{space 2} .4541961{col 55}{space 1}    7.39{col 64}{space 3}0.000{col 72}{space 4} 2.467231{col 85}{space 3} 4.247647
{txt}{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}/lnalpha {c |}{col 32}{res}{space 2}-2.041688{col 44}{space 2} .1998543{col 72}{space 4}-2.433395{col 85}{space 3}-1.649981
{txt}{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                         alpha {c |}{col 32}{res}{space 2} .1298094{col 44}{space 2}  .025943{col 72}{space 4} .0877384{col 85}{space 3} .1920536
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. eststo: nbreg days_to_category1 intl_student_pc_china2018 migration_from_china_pc2019 migration_total2019 log_gdp log_gdp_pc log_resource_pc polity2 health_2018 distcap_china deaths_pc if date == 22001, vce(robust)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-957.48315}  
Iteration 1:{space 3}log pseudolikelihood = {res:-957.41449}  
Iteration 2:{space 3}log pseudolikelihood = {res:-957.41445}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-755.43727}  
Iteration 1:{space 3}log pseudolikelihood = {res:-714.76592}  
Iteration 2:{space 3}log pseudolikelihood = {res:-679.69272}  
Iteration 3:{space 3}log pseudolikelihood = {res:-679.68745}  
Iteration 4:{space 3}log pseudolikelihood = {res:-679.68745}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-668.77891}  
Iteration 1:{space 3}log pseudolikelihood = {res:-667.72285}  
Iteration 2:{space 3}log pseudolikelihood = {res: -667.7121}  
Iteration 3:{space 3}log pseudolikelihood = {res:-667.71209}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       148
{txt}{col 49}Wald chi2({res}10{txt}){col 67}= {res}     43.51
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-667.71209{txt}{col 49}Pseudo R2{col 67}= {res}    0.0176

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}          days_to_category1{col 29}{c |}      Coef.{col 41}   Std. Err.{col 53}      z{col 61}   P>|z|{col 69}     [95% Con{col 82}f. Interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}intl_student_pc_china2018 {c |}{col 29}{res}{space 2}-.0550028{col 41}{space 2} .0625839{col 52}{space 1}   -0.88{col 61}{space 3}0.379{col 69}{space 4}-.1776649{col 82}{space 3} .0676593
{txt}migration_from_china_pc2019 {c |}{col 29}{res}{space 2}-.0264071{col 41}{space 2} .0070705{col 52}{space 1}   -3.73{col 61}{space 3}0.000{col 69}{space 4}-.0402649{col 82}{space 3}-.0125492
{txt}{space 8}migration_total2019 {c |}{col 29}{res}{space 2}-.0009121{col 41}{space 2} .0030074{col 52}{space 1}   -0.30{col 61}{space 3}0.762{col 69}{space 4}-.0068065{col 82}{space 3} .0049824
{txt}{space 20}log_gdp {c |}{col 29}{res}{space 2} .0288176{col 41}{space 2} .0245357{col 52}{space 1}    1.17{col 61}{space 3}0.240{col 69}{space 4}-.0192715{col 82}{space 3} .0769067
{txt}{space 17}log_gdp_pc {c |}{col 29}{res}{space 2} .0041385{col 41}{space 2} .0403482{col 52}{space 1}    0.10{col 61}{space 3}0.918{col 69}{space 4}-.0749426{col 82}{space 3} .0832196
{txt}{space 12}log_resource_pc {c |}{col 29}{res}{space 2}-.0282675{col 41}{space 2}  .016752{col 52}{space 1}   -1.69{col 61}{space 3}0.092{col 69}{space 4}-.0611009{col 82}{space 3} .0045658
{txt}{space 20}polity2 {c |}{col 29}{res}{space 2}-.0021589{col 41}{space 2} .0067715{col 52}{space 1}   -0.32{col 61}{space 3}0.750{col 69}{space 4}-.0154308{col 82}{space 3} .0111131
{txt}{space 16}health_2018 {c |}{col 29}{res}{space 2} .0069758{col 41}{space 2}  .018303{col 52}{space 1}    0.38{col 61}{space 3}0.703{col 69}{space 4}-.0288974{col 82}{space 3}  .042849
{txt}{space 14}distcap_china {c |}{col 29}{res}{space 2} .0177789{col 41}{space 2} .0094653{col 52}{space 1}    1.88{col 61}{space 3}0.060{col 69}{space 4}-.0007728{col 82}{space 3} .0363306
{txt}{space 18}deaths_pc {c |}{col 29}{res}{space 2} .0019059{col 41}{space 2} .0009785{col 52}{space 1}    1.95{col 61}{space 3}0.051{col 69}{space 4}-.0000119{col 82}{space 3} .0038238
{txt}{space 22}_cons {c |}{col 29}{res}{space 2} 3.287431{col 41}{space 2}  .462241{col 52}{space 1}    7.11{col 61}{space 3}0.000{col 69}{space 4} 2.381456{col 82}{space 3} 4.193407
{txt}{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 19}/lnalpha {c |}{col 29}{res}{space 2}-2.043207{col 41}{space 2} .2016736{col 69}{space 4} -2.43848{col 82}{space 3}-1.647934
{txt}{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                      alpha {c |}{col 29}{res}{space 2} .1296124{col 41}{space 2} .0261394{col 69}{space 4} .0872935{col 82}{space 3} .1924471
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}. eststo: nbreg days_to_category1 contract_workers_abroad_2018_pc labor_services_workers_2018_pc intl_student_pc_china2018 migration_from_china_pc2019 migration_total2019 log_gdp log_gdp_pc log_resource_pc polity2 health_2018 distcap_china deaths_pc if date == 22001, vce(robust)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-921.57686}  
Iteration 1:{space 3}log pseudolikelihood = {res:-921.44193}  
Iteration 2:{space 3}log pseudolikelihood = {res: -921.4417}  
Iteration 3:{space 3}log pseudolikelihood = {res: -921.4417}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-755.43727}  
Iteration 1:{space 3}log pseudolikelihood = {res:-714.76592}  
Iteration 2:{space 3}log pseudolikelihood = {res:-679.69272}  
Iteration 3:{space 3}log pseudolikelihood = {res:-679.68745}  
Iteration 4:{space 3}log pseudolikelihood = {res:-679.68745}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-664.67451}  
Iteration 1:{space 3}log pseudolikelihood = {res:-662.60781}  
Iteration 2:{space 3}log pseudolikelihood = {res:-662.56183}  
Iteration 3:{space 3}log pseudolikelihood = {res: -662.5618}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       148
{txt}{col 49}Wald chi2({res}12{txt}){col 67}= {res}    153.81
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res} -662.5618{txt}{col 49}Pseudo R2{col 67}= {res}    0.0252

{txt}{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}              days_to_category1{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      z{col 65}   P>|z|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
contract_workers_abroad_2018_pc {c |}{col 33}{res}{space 2}-.3581032{col 45}{space 2} .1139644{col 56}{space 1}   -3.14{col 65}{space 3}0.002{col 73}{space 4}-.5814693{col 86}{space 3} -.134737
{txt}{space 1}labor_services_workers_2018_pc {c |}{col 33}{res}{space 2}-.0304655{col 45}{space 2} .0510301{col 56}{space 1}   -0.60{col 65}{space 3}0.551{col 73}{space 4}-.1304827{col 86}{space 3} .0695517
{txt}{space 6}intl_student_pc_china2018 {c |}{col 33}{res}{space 2} -.118756{col 45}{space 2} .0941191{col 56}{space 1}   -1.26{col 65}{space 3}0.207{col 73}{space 4}-.3032261{col 86}{space 3} .0657142
{txt}{space 4}migration_from_china_pc2019 {c |}{col 33}{res}{space 2}-.0168227{col 45}{space 2} .0189174{col 56}{space 1}   -0.89{col 65}{space 3}0.374{col 73}{space 4}   -.0539{col 86}{space 3} .0202547
{txt}{space 12}migration_total2019 {c |}{col 33}{res}{space 2} .0011048{col 45}{space 2} .0026218{col 56}{space 1}    0.42{col 65}{space 3}0.673{col 73}{space 4}-.0040339{col 86}{space 3} .0062436
{txt}{space 24}log_gdp {c |}{col 33}{res}{space 2} .0161415{col 45}{space 2} .0229563{col 56}{space 1}    0.70{col 65}{space 3}0.482{col 73}{space 4}-.0288519{col 86}{space 3}  .061135
{txt}{space 21}log_gdp_pc {c |}{col 33}{res}{space 2}  .010098{col 45}{space 2} .0377526{col 56}{space 1}    0.27{col 65}{space 3}0.789{col 73}{space 4}-.0638957{col 86}{space 3} .0840918
{txt}{space 16}log_resource_pc {c |}{col 33}{res}{space 2}-.0162783{col 45}{space 2} .0159085{col 56}{space 1}   -1.02{col 65}{space 3}0.306{col 73}{space 4}-.0474585{col 86}{space 3} .0149018
{txt}{space 24}polity2 {c |}{col 33}{res}{space 2}-.0051952{col 45}{space 2} .0067562{col 56}{space 1}   -0.77{col 65}{space 3}0.442{col 73}{space 4}-.0184371{col 86}{space 3} .0080468
{txt}{space 20}health_2018 {c |}{col 33}{res}{space 2} .0014675{col 45}{space 2} .0175418{col 56}{space 1}    0.08{col 65}{space 3}0.933{col 73}{space 4}-.0329138{col 86}{space 3} .0358488
{txt}{space 18}distcap_china {c |}{col 33}{res}{space 2} .0184064{col 45}{space 2} .0093219{col 56}{space 1}    1.97{col 65}{space 3}0.048{col 73}{space 4} .0001359{col 86}{space 3}  .036677
{txt}{space 22}deaths_pc {c |}{col 33}{res}{space 2} .0017482{col 45}{space 2}  .000977{col 56}{space 1}    1.79{col 65}{space 3}0.074{col 73}{space 4}-.0001666{col 86}{space 3}  .003663
{txt}{space 26}_cons {c |}{col 33}{res}{space 2} 3.583053{col 45}{space 2} .4376434{col 56}{space 1}    8.19{col 65}{space 3}0.000{col 73}{space 4} 2.725287{col 86}{space 3} 4.440818
{txt}{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}/lnalpha {c |}{col 33}{res}{space 2}-2.124366{col 45}{space 2} .2096888{col 73}{space 4}-2.535349{col 86}{space 3}-1.713384
{txt}{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                          alpha {c |}{col 33}{res}{space 2} .1195087{col 45}{space 2} .0250596{col 73}{space 4} .0792341{col 86}{space 3} .1802548
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{com}. eststo: nbreg days_to_category1 contract_workers_abroad_2018_pc labor_services_workers_2018_pc intl_student_pc_china2018 migration_from_china_pc2019 migration_total2019 log_gdp log_gdp_pc log_resource_pc polity2 health_2018 distcap_china fdi_china_inward_pc_2018 trade_china_gdp_2018 deaths_pc if date == 22001, vce(robust)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-749.88208}  
Iteration 1:{space 3}log pseudolikelihood = {res:-749.48589}  
Iteration 2:{space 3}log pseudolikelihood = {res:-749.48501}  
Iteration 3:{space 3}log pseudolikelihood = {res:-749.48501}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-623.13116}  
Iteration 1:{space 3}log pseudolikelihood = {res:-589.95563}  
Iteration 2:{space 3}log pseudolikelihood = {res:-562.49177}  
Iteration 3:{space 3}log pseudolikelihood = {res: -562.4895}  
Iteration 4:{space 3}log pseudolikelihood = {res: -562.4895}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-547.53675}  
Iteration 1:{space 3}log pseudolikelihood = {res:-544.95372}  
Iteration 2:{space 3}log pseudolikelihood = {res:-544.84365}  
Iteration 3:{space 3}log pseudolikelihood = {res:-544.84349}  
Iteration 4:{space 3}log pseudolikelihood = {res:-544.84349}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       122
{txt}{col 49}Wald chi2({res}14{txt}){col 67}= {res}   1064.15
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-544.84349{txt}{col 49}Pseudo R2{col 67}= {res}    0.0314

{txt}{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}              days_to_category1{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      z{col 65}   P>|z|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
contract_workers_abroad_2018_pc {c |}{col 33}{res}{space 2}-.5401678{col 45}{space 2} .1514817{col 56}{space 1}   -3.57{col 65}{space 3}0.000{col 73}{space 4}-.8370665{col 86}{space 3}-.2432691
{txt}{space 1}labor_services_workers_2018_pc {c |}{col 33}{res}{space 2}-.0672528{col 45}{space 2} .0919304{col 56}{space 1}   -0.73{col 65}{space 3}0.464{col 73}{space 4}-.2474331{col 86}{space 3} .1129274
{txt}{space 6}intl_student_pc_china2018 {c |}{col 33}{res}{space 2}-.2003583{col 45}{space 2} .1528311{col 56}{space 1}   -1.31{col 65}{space 3}0.190{col 73}{space 4}-.4999018{col 86}{space 3} .0991851
{txt}{space 4}migration_from_china_pc2019 {c |}{col 33}{res}{space 2}-.0032735{col 45}{space 2} .0319477{col 56}{space 1}   -0.10{col 65}{space 3}0.918{col 73}{space 4}-.0658899{col 86}{space 3} .0593428
{txt}{space 12}migration_total2019 {c |}{col 33}{res}{space 2} .0028469{col 45}{space 2} .0032845{col 56}{space 1}    0.87{col 65}{space 3}0.386{col 73}{space 4}-.0035906{col 86}{space 3} .0092844
{txt}{space 24}log_gdp {c |}{col 33}{res}{space 2} .0029328{col 45}{space 2} .0258522{col 56}{space 1}    0.11{col 65}{space 3}0.910{col 73}{space 4}-.0477366{col 86}{space 3} .0536021
{txt}{space 21}log_gdp_pc {c |}{col 33}{res}{space 2} .0017325{col 45}{space 2} .0442271{col 56}{space 1}    0.04{col 65}{space 3}0.969{col 73}{space 4}-.0849509{col 86}{space 3}  .088416
{txt}{space 16}log_resource_pc {c |}{col 33}{res}{space 2} .0018514{col 45}{space 2} .0172082{col 56}{space 1}    0.11{col 65}{space 3}0.914{col 73}{space 4}-.0318761{col 86}{space 3} .0355789
{txt}{space 24}polity2 {c |}{col 33}{res}{space 2} .0009761{col 45}{space 2} .0088225{col 56}{space 1}    0.11{col 65}{space 3}0.912{col 73}{space 4}-.0163157{col 86}{space 3}  .018268
{txt}{space 20}health_2018 {c |}{col 33}{res}{space 2}-.0028018{col 45}{space 2} .0228318{col 56}{space 1}   -0.12{col 65}{space 3}0.902{col 73}{space 4}-.0475513{col 86}{space 3} .0419478
{txt}{space 18}distcap_china {c |}{col 33}{res}{space 2} .0118151{col 45}{space 2} .0093509{col 56}{space 1}    1.26{col 65}{space 3}0.206{col 73}{space 4}-.0065124{col 86}{space 3} .0301426
{txt}{space 7}fdi_china_inward_pc_2018 {c |}{col 33}{res}{space 2}-7.68e-07{col 45}{space 2} 9.27e-06{col 56}{space 1}   -0.08{col 65}{space 3}0.934{col 73}{space 4}-.0000189{col 86}{space 3} .0000174
{txt}{space 11}trade_china_gdp_2018 {c |}{col 33}{res}{space 2}-.0031001{col 45}{space 2} .0037835{col 56}{space 1}   -0.82{col 65}{space 3}0.413{col 73}{space 4}-.0105157{col 86}{space 3} .0043154
{txt}{space 22}deaths_pc {c |}{col 33}{res}{space 2} .0016072{col 45}{space 2} .0010312{col 56}{space 1}    1.56{col 65}{space 3}0.119{col 73}{space 4}-.0004138{col 86}{space 3} .0036283
{txt}{space 26}_cons {c |}{col 33}{res}{space 2} 3.996627{col 45}{space 2} .4856923{col 56}{space 1}    8.23{col 65}{space 3}0.000{col 73}{space 4} 3.044688{col 86}{space 3} 4.948567
{txt}{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}/lnalpha {c |}{col 33}{res}{space 2}-2.151072{col 45}{space 2} .2408752{col 73}{space 4}-2.623179{col 86}{space 3}-1.678965
{txt}{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                          alpha {c |}{col 33}{res}{space 2} .1163594{col 45}{space 2} .0280281{col 73}{space 4} .0725718{col 86}{space 3} .1865669
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{com}. eststo: nbreg days_to_category1 contract_workers_abroad_2018_pc labor_services_workers_2018_pc intl_student_pc_china2018 migration_from_china_pc2019 migration_total2019 log_gdp log_gdp_pc log_resource_pc polity2 health_2018 distcap_china fdi_china_inward_pc_2018 trade_china_gdp_2018 trade_nonchina_gdp_2018 deaths_pc if date == 22001, vce(robust)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -736.2255}  
Iteration 1:{space 3}log pseudolikelihood = {res:-735.89828}  
Iteration 2:{space 3}log pseudolikelihood = {res:-735.89737}  
Iteration 3:{space 3}log pseudolikelihood = {res:-735.89737}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-623.13116}  
Iteration 1:{space 3}log pseudolikelihood = {res:-589.95563}  
Iteration 2:{space 3}log pseudolikelihood = {res:-562.49177}  
Iteration 3:{space 3}log pseudolikelihood = {res: -562.4895}  
Iteration 4:{space 3}log pseudolikelihood = {res: -562.4895}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-546.05408}  
Iteration 1:{space 3}log pseudolikelihood = {res:-542.95239}  
Iteration 2:{space 3}log pseudolikelihood = {res: -542.7894}  
Iteration 3:{space 3}log pseudolikelihood = {res:-542.78904}  
Iteration 4:{space 3}log pseudolikelihood = {res:-542.78904}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       122
{txt}{col 49}Wald chi2({res}15{txt}){col 67}= {res}   1649.03
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-542.78904{txt}{col 49}Pseudo R2{col 67}= {res}    0.0350

{txt}{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}              days_to_category1{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      z{col 65}   P>|z|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
contract_workers_abroad_2018_pc {c |}{col 33}{res}{space 2}-.5213106{col 45}{space 2} .1462289{col 56}{space 1}   -3.57{col 65}{space 3}0.000{col 73}{space 4}-.8079141{col 86}{space 3}-.2347072
{txt}{space 1}labor_services_workers_2018_pc {c |}{col 33}{res}{space 2}-.0749175{col 45}{space 2} .0936721{col 56}{space 1}   -0.80{col 65}{space 3}0.424{col 73}{space 4}-.2585114{col 86}{space 3} .1086764
{txt}{space 6}intl_student_pc_china2018 {c |}{col 33}{res}{space 2}-.1628504{col 45}{space 2} .1575331{col 56}{space 1}   -1.03{col 65}{space 3}0.301{col 73}{space 4}-.4716096{col 86}{space 3} .1459087
{txt}{space 4}migration_from_china_pc2019 {c |}{col 33}{res}{space 2}-.0048046{col 45}{space 2} .0324324{col 56}{space 1}   -0.15{col 65}{space 3}0.882{col 73}{space 4} -.068371{col 86}{space 3} .0587618
{txt}{space 12}migration_total2019 {c |}{col 33}{res}{space 2} .0028153{col 45}{space 2} .0029393{col 56}{space 1}    0.96{col 65}{space 3}0.338{col 73}{space 4}-.0029456{col 86}{space 3} .0085762
{txt}{space 24}log_gdp {c |}{col 33}{res}{space 2} .0236409{col 45}{space 2} .0266839{col 56}{space 1}    0.89{col 65}{space 3}0.376{col 73}{space 4}-.0286585{col 86}{space 3} .0759403
{txt}{space 21}log_gdp_pc {c |}{col 33}{res}{space 2}-.0397001{col 45}{space 2} .0462462{col 56}{space 1}   -0.86{col 65}{space 3}0.391{col 73}{space 4}-.1303409{col 86}{space 3} .0509407
{txt}{space 16}log_resource_pc {c |}{col 33}{res}{space 2} .0043058{col 45}{space 2} .0169091{col 56}{space 1}    0.25{col 65}{space 3}0.799{col 73}{space 4}-.0288354{col 86}{space 3} .0374471
{txt}{space 24}polity2 {c |}{col 33}{res}{space 2} .0018067{col 45}{space 2} .0082937{col 56}{space 1}    0.22{col 65}{space 3}0.828{col 73}{space 4}-.0144487{col 86}{space 3}  .018062
{txt}{space 20}health_2018 {c |}{col 33}{res}{space 2}-.0023487{col 45}{space 2} .0224961{col 56}{space 1}   -0.10{col 65}{space 3}0.917{col 73}{space 4}-.0464402{col 86}{space 3} .0417429
{txt}{space 18}distcap_china {c |}{col 33}{res}{space 2} .0171365{col 45}{space 2} .0098861{col 56}{space 1}    1.73{col 65}{space 3}0.083{col 73}{space 4}-.0022398{col 86}{space 3} .0365128
{txt}{space 7}fdi_china_inward_pc_2018 {c |}{col 33}{res}{space 2} 5.17e-06{col 45}{space 2} 9.64e-06{col 56}{space 1}    0.54{col 65}{space 3}0.592{col 73}{space 4}-.0000137{col 86}{space 3} .0000241
{txt}{space 11}trade_china_gdp_2018 {c |}{col 33}{res}{space 2}-.0045444{col 45}{space 2} .0038136{col 56}{space 1}   -1.19{col 65}{space 3}0.233{col 73}{space 4} -.012019{col 86}{space 3} .0029302
{txt}{space 8}trade_nonchina_gdp_2018 {c |}{col 33}{res}{space 2} .0024636{col 45}{space 2} .0009552{col 56}{space 1}    2.58{col 65}{space 3}0.010{col 73}{space 4} .0005914{col 86}{space 3} .0043358
{txt}{space 22}deaths_pc {c |}{col 33}{res}{space 2} .0017732{col 45}{space 2} .0010158{col 56}{space 1}    1.75{col 65}{space 3}0.081{col 73}{space 4}-.0002178{col 86}{space 3} .0037642
{txt}{space 26}_cons {c |}{col 33}{res}{space 2} 3.628389{col 45}{space 2} .5083208{col 56}{space 1}    7.14{col 65}{space 3}0.000{col 73}{space 4} 2.632098{col 86}{space 3} 4.624679
{txt}{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}/lnalpha {c |}{col 33}{res}{space 2}-2.191465{col 45}{space 2}  .238843{col 73}{space 4}-2.659588{col 86}{space 3}-1.723341
{txt}{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                          alpha {c |}{col 33}{res}{space 2} .1117529{col 45}{space 2} .0266914{col 73}{space 4}  .069977{col 86}{space 3} .1784689
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est6{txt} stored)

{com}. 
. esttab using "TableA4.tex", label keep(contract_workers_abroad_2018_pc labor_services_workers_2018_pc intl_student_pc_china2018 migration_from_china_pc2019 migration_total2019 fdi_china_inward_pc_2018 log_gdp log_gdp_pc trade_china_gdp_2018 trade_nonchina_gdp_2018 log_resource_pc polity2 health_2018 distcap_china deaths_pc) order(contract_workers_abroad_2018_pc labor_services_workers_2018_pc intl_student_pc_china2018 migration_from_china_pc2019 migration_total2019 fdi_china_inward_pc_2018 log_gdp log_gdp_pc trade_china_gdp_2018 trade_nonchina_gdp_2018 log_resource_pc polity2 health_2018 distcap_china deaths_pc) nonotes se(3) b(3) replace star(+ 0.10 * 0.05 ** 0.01) compress nogaps nodepvars mtitles("" "" "" "" "" "") 
{res}{txt}(note: file TableA4.tex not found)
(output written to {browse  `"TableA4.tex"'})

{com}. 
. * Table A6: Impact of Entry Restrictions on COVID-19 Confirmed Cases and Deaths
. 
. xtset cowcode date
{res}{txt}{col 8}panel variable:  {res}cowcode (strongly balanced)
{txt}{col 9}time variable:  {res}{col 25}date, 15jan2020 to 27mar2020
{txt}{col 17}delta:  {res}1 day
{txt}
{com}. 
. foreach var of varlist be_cat_1_China_carryover be_cat_2_China_carryover be_cat_3_China_carryover  {c -(}
{txt}  2{com}.         gen L7`var' = L7.`var'
{txt}  3{com}.         gen L14`var' = L14.`var'
{txt}  4{com}.         gen L21`var' = L21.`var'
{txt}  5{com}.  
. {c )-}
{txt}(1,365 missing values generated)
(2,730 missing values generated)
(4,095 missing values generated)
(1,365 missing values generated)
(2,730 missing values generated)
(4,095 missing values generated)
(1,365 missing values generated)
(2,730 missing values generated)
(4,095 missing values generated)

{com}. 
. label var L7be_cat_1_China_carryover "Category 1 (t-7)"
{txt}
{com}. label var L7be_cat_2_China_carryover "Category 2 (t-7)"
{txt}
{com}. label var L7be_cat_3_China_carryover  "Category 3 (t-7)"
{txt}
{com}. 
. label var L14be_cat_1_China_carryover "Category 1 (t-14)"
{txt}
{com}. label var L14be_cat_2_China_carryover "Category 2 (t-14)"
{txt}
{com}. label var L14be_cat_3_China_carryover  "Category 3 (t-14)"
{txt}
{com}. 
. label var L21be_cat_1_China_carryover "Category 1 (t-21)"
{txt}
{com}. label var L21be_cat_2_China_carryover "Category 2 (t-21)"
{txt}
{com}. label var L21be_cat_3_China_carryover  "Category 3 (t-21)"
{txt}
{com}. 
. 
. eststo clear
{txt}
{com}. eststo: xi: regress confirmed_7_day_pc L7be_cat_1_China_carryover L7be_cat_2_China_carryover L7be_cat_3_China_carryover  time time_sq time_cube i.cowcode
{txt}i.cowcode{col 19}_Icowcode_2-1027{col 39}(naturally coded; _Icowcode_2 omitted)
note: _Icowcode_531 omitted because of collinearity
note: _Icowcode_713 omitted because of collinearity
note: _Icowcode_1027 omitted because of collinearity

      Source {c |}       SS           df       MS      Number of obs   ={res}    12,672
{txt}{hline 13}{c +}{hline 34}   F(197, 12474)   = {res}    27.85
{txt}       Model {c |} {res} 810906.445       197  4116.27637   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 1843954.22    12,474  147.823811   {txt}R-squared       ={res}    0.3054
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.2945
{txt}       Total {c |} {res} 2654860.67    12,671  209.522584   {txt}Root MSE        =   {res} 12.158

{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        confirmed_7_day_pc{col 28}{c |}      Coef.{col 40}   Std. Err.{col 52}      t{col 60}   P>|t|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
L7be_cat_1_China_carryover {c |}{col 28}{res}{space 2}-4.178561{col 40}{space 2} .4687077{col 51}{space 1}   -8.92{col 60}{space 3}0.000{col 68}{space 4}-5.097301{col 81}{space 3}-3.259822
{txt}L7be_cat_2_China_carryover {c |}{col 28}{res}{space 2}-1.927265{col 40}{space 2} 1.202956{col 51}{space 1}   -1.60{col 60}{space 3}0.109{col 68}{space 4}-4.285244{col 81}{space 3} .4307141
{txt}L7be_cat_3_China_carryover {c |}{col 28}{res}{space 2} -2.43274{col 40}{space 2} .4819798{col 51}{space 1}   -5.05{col 60}{space 3}0.000{col 68}{space 4}-3.377495{col 81}{space 3}-1.487985
{txt}{space 22}time {c |}{col 28}{res}{space 2} .4684654{col 40}{space 2} .0896536{col 51}{space 1}    5.23{col 60}{space 3}0.000{col 68}{space 4} .2927305{col 81}{space 3} .6442003
{txt}{space 19}time_sq {c |}{col 28}{res}{space 2}-.0165154{col 40}{space 2} .0024477{col 51}{space 1}   -6.75{col 60}{space 3}0.000{col 68}{space 4}-.0213132{col 81}{space 3}-.0117175
{txt}{space 17}time_cube {c |}{col 28}{res}{space 2} .0001868{col 40}{space 2} .0000199{col 51}{space 1}    9.36{col 60}{space 3}0.000{col 68}{space 4} .0001477{col 81}{space 3} .0002259
{txt}{space 14}_Icowcode_20 {c |}{col 28}{res}{space 2}-4.705727{col 40}{space 2}  2.14376{col 51}{space 1}   -2.20{col 60}{space 3}0.028{col 68}{space 4}-8.907827{col 81}{space 3}-.5036267
{txt}{space 14}_Icowcode_31 {c |}{col 28}{res}{space 2}-2.391471{col 40}{space 2} 2.116592{col 51}{space 1}   -1.13{col 60}{space 3}0.259{col 68}{space 4}-6.540319{col 81}{space 3} 1.757376
{txt}{space 14}_Icowcode_40 {c |}{col 28}{res}{space 2}-5.784048{col 40}{space 2}  2.14376{col 51}{space 1}   -2.70{col 60}{space 3}0.007{col 68}{space 4}-9.986148{col 81}{space 3}-1.581947
{txt}{space 14}_Icowcode_41 {c |}{col 28}{res}{space 2}-5.082766{col 40}{space 2}  2.13187{col 51}{space 1}   -2.38{col 60}{space 3}0.017{col 68}{space 4} -9.26156{col 81}{space 3}-.9039714
{txt}{space 14}_Icowcode_42 {c |}{col 28}{res}{space 2}-5.375509{col 40}{space 2}  2.14376{col 51}{space 1}   -2.51{col 60}{space 3}0.012{col 68}{space 4} -9.57761{col 81}{space 3}-1.173409
{txt}{space 14}_Icowcode_51 {c |}{col 28}{res}{space 2}-2.572066{col 40}{space 2} 2.116533{col 51}{space 1}   -1.22{col 60}{space 3}0.224{col 68}{space 4}-6.720797{col 81}{space 3} 1.576665
{txt}{space 14}_Icowcode_52 {c |}{col 28}{res}{space 2} -3.77449{col 40}{space 2} 2.123919{col 51}{space 1}   -1.78{col 60}{space 3}0.076{col 68}{space 4}-7.937698{col 81}{space 3} .3887179
{txt}{space 14}_Icowcode_53 {c |}{col 28}{res}{space 2} -4.65035{col 40}{space 2} 2.143812{col 51}{space 1}   -2.17{col 60}{space 3}0.030{col 68}{space 4}-8.852553{col 81}{space 3}-.4481469
{txt}{space 14}_Icowcode_54 {c |}{col 28}{res}{space 2}-2.795722{col 40}{space 2} 2.175317{col 51}{space 1}   -1.29{col 60}{space 3}0.199{col 68}{space 4}-7.059678{col 81}{space 3} 1.468234
{txt}{space 14}_Icowcode_55 {c |}{col 28}{res}{space 2}-1.945923{col 40}{space 2} 2.118953{col 51}{space 1}   -0.92{col 60}{space 3}0.358{col 68}{space 4}-6.099397{col 81}{space 3} 2.207552
{txt}{space 14}_Icowcode_56 {c |}{col 28}{res}{space 2}-1.901136{col 40}{space 2} 2.122523{col 51}{space 1}   -0.90{col 60}{space 3}0.370{col 68}{space 4}-6.061609{col 81}{space 3} 2.259337
{txt}{space 14}_Icowcode_58 {c |}{col 28}{res}{space 2}-2.122908{col 40}{space 2} 2.116533{col 51}{space 1}   -1.00{col 60}{space 3}0.316{col 68}{space 4}-6.271638{col 81}{space 3} 2.025823
{txt}{space 14}_Icowcode_60 {c |}{col 28}{res}{space 2}-3.796708{col 40}{space 2} 2.168284{col 51}{space 1}   -1.75{col 60}{space 3}0.080{col 68}{space 4}-8.046878{col 81}{space 3} .4534631
{txt}{space 14}_Icowcode_70 {c |}{col 28}{res}{space 2}-5.022981{col 40}{space 2} 2.147014{col 51}{space 1}   -2.34{col 60}{space 3}0.019{col 68}{space 4} -9.23146{col 81}{space 3}-.8145026
{txt}{space 14}_Icowcode_80 {c |}{col 28}{res}{space 2}-3.146195{col 40}{space 2} 2.116914{col 51}{space 1}   -1.49{col 60}{space 3}0.137{col 68}{space 4}-7.295673{col 81}{space 3} 1.003283
{txt}{space 14}_Icowcode_90 {c |}{col 28}{res}{space 2} -2.66558{col 40}{space 2} 2.116533{col 51}{space 1}   -1.26{col 60}{space 3}0.208{col 68}{space 4} -6.81431{col 81}{space 3} 1.483151
{txt}{space 14}_Icowcode_91 {c |}{col 28}{res}{space 2}-4.114835{col 40}{space 2} 2.143157{col 51}{space 1}   -1.92{col 60}{space 3}0.055{col 68}{space 4}-8.315754{col 81}{space 3} .0860837
{txt}{space 14}_Icowcode_92 {c |}{col 28}{res}{space 2}-2.668387{col 40}{space 2} 2.116533{col 51}{space 1}   -1.26{col 60}{space 3}0.207{col 68}{space 4}-6.817118{col 81}{space 3} 1.480343
{txt}{space 14}_Icowcode_93 {c |}{col 28}{res}{space 2}-5.845796{col 40}{space 2}  2.14376{col 51}{space 1}   -2.73{col 60}{space 3}0.006{col 68}{space 4} -10.0479{col 81}{space 3}-1.643695
{txt}{space 14}_Icowcode_94 {c |}{col 28}{res}{space 2}-4.521822{col 40}{space 2} 2.147014{col 51}{space 1}   -2.11{col 60}{space 3}0.035{col 68}{space 4}  -8.7303{col 81}{space 3}-.3133427
{txt}{space 14}_Icowcode_95 {c |}{col 28}{res}{space 2}-2.589806{col 40}{space 2} 2.167196{col 51}{space 1}   -1.20{col 60}{space 3}0.232{col 68}{space 4}-6.837843{col 81}{space 3} 1.658232
{txt}{space 13}_Icowcode_100 {c |}{col 28}{res}{space 2}-5.737917{col 40}{space 2}  2.14376{col 51}{space 1}   -2.68{col 60}{space 3}0.007{col 68}{space 4}-9.940017{col 81}{space 3}-1.535816
{txt}{space 13}_Icowcode_101 {c |}{col 28}{res}{space 2}-4.957276{col 40}{space 2} 2.147895{col 51}{space 1}   -2.31{col 60}{space 3}0.021{col 68}{space 4}-9.167481{col 81}{space 3}-.7470703
{txt}{space 13}_Icowcode_110 {c |}{col 28}{res}{space 2}-5.335361{col 40}{space 2} 2.143812{col 51}{space 1}   -2.49{col 60}{space 3}0.013{col 68}{space 4}-9.537564{col 81}{space 3}-1.133158
{txt}{space 13}_Icowcode_115 {c |}{col 28}{res}{space 2}-2.832359{col 40}{space 2} 2.116592{col 51}{space 1}   -1.34{col 60}{space 3}0.181{col 68}{space 4}-6.981207{col 81}{space 3} 1.316488
{txt}{space 13}_Icowcode_130 {c |}{col 28}{res}{space 2}-4.085683{col 40}{space 2} 2.147442{col 51}{space 1}   -1.90{col 60}{space 3}0.057{col 68}{space 4}-8.295001{col 81}{space 3}  .123635
{txt}{space 13}_Icowcode_135 {c |}{col 28}{res}{space 2}-5.634839{col 40}{space 2}  2.14376{col 51}{space 1}   -2.63{col 60}{space 3}0.009{col 68}{space 4} -9.83694{col 81}{space 3}-1.432739
{txt}{space 13}_Icowcode_140 {c |}{col 28}{res}{space 2}-5.688312{col 40}{space 2}  2.14376{col 51}{space 1}   -2.65{col 60}{space 3}0.008{col 68}{space 4}-9.890413{col 81}{space 3}-1.486212
{txt}{space 13}_Icowcode_145 {c |}{col 28}{res}{space 2}-5.362499{col 40}{space 2} 2.136419{col 51}{space 1}   -2.51{col 60}{space 3}0.012{col 68}{space 4} -9.55021{col 81}{space 3}-1.174787
{txt}{space 13}_Icowcode_150 {c |}{col 28}{res}{space 2}-4.934564{col 40}{space 2} 2.147895{col 51}{space 1}   -2.30{col 60}{space 3}0.022{col 68}{space 4}-9.144769{col 81}{space 3}-.7243586
{txt}{space 13}_Icowcode_155 {c |}{col 28}{res}{space 2}-4.751126{col 40}{space 2} 2.143627{col 51}{space 1}   -2.22{col 60}{space 3}0.027{col 68}{space 4}-8.952964{col 81}{space 3}-.5492867
{txt}{space 13}_Icowcode_160 {c |}{col 28}{res}{space 2}-5.355891{col 40}{space 2} 2.143943{col 51}{space 1}   -2.50{col 60}{space 3}0.012{col 68}{space 4}-9.558349{col 81}{space 3}-1.153433
{txt}{space 13}_Icowcode_165 {c |}{col 28}{res}{space 2}-4.694021{col 40}{space 2} 2.143707{col 51}{space 1}   -2.19{col 60}{space 3}0.029{col 68}{space 4}-8.896017{col 81}{space 3}-.4920243
{txt}{space 13}_Icowcode_200 {c |}{col 28}{res}{space 2}-.6362719{col 40}{space 2} 2.163084{col 51}{space 1}   -0.29{col 60}{space 3}0.769{col 68}{space 4} -4.87625{col 81}{space 3} 3.603706
{txt}{space 13}_Icowcode_205 {c |}{col 28}{res}{space 2}-1.559553{col 40}{space 2}  2.14376{col 51}{space 1}   -0.73{col 60}{space 3}0.467{col 68}{space 4}-5.761653{col 81}{space 3} 2.642548
{txt}{space 13}_Icowcode_210 {c |}{col 28}{res}{space 2}-.7312829{col 40}{space 2}  2.14376{col 51}{space 1}   -0.34{col 60}{space 3}0.733{col 68}{space 4}-4.933383{col 81}{space 3} 3.470817
{txt}{space 13}_Icowcode_211 {c |}{col 28}{res}{space 2} .3371326{col 40}{space 2}  2.14376{col 51}{space 1}    0.16{col 60}{space 3}0.875{col 68}{space 4}-3.864968{col 81}{space 3} 4.539233
{txt}{space 13}_Icowcode_212 {c |}{col 28}{res}{space 2} 22.03758{col 40}{space 2}  2.14376{col 51}{space 1}   10.28{col 60}{space 3}0.000{col 68}{space 4} 17.83548{col 81}{space 3} 26.23968
{txt}{space 13}_Icowcode_220 {c |}{col 28}{res}{space 2}-.6149922{col 40}{space 2}  2.14376{col 51}{space 1}   -0.29{col 60}{space 3}0.774{col 68}{space 4}-4.817093{col 81}{space 3} 3.587108
{txt}{space 13}_Icowcode_223 {c |}{col 28}{res}{space 2}   13.505{col 40}{space 2}  2.14376{col 51}{space 1}    6.30{col 60}{space 3}0.000{col 68}{space 4} 9.302897{col 81}{space 3}  17.7071
{txt}{space 13}_Icowcode_225 {c |}{col 28}{res}{space 2}  11.5294{col 40}{space 2}  2.14376{col 51}{space 1}    5.38{col 60}{space 3}0.000{col 68}{space 4} 7.327299{col 81}{space 3}  15.7315
{txt}{space 13}_Icowcode_230 {c |}{col 28}{res}{space 2} 8.126528{col 40}{space 2}  2.14376{col 51}{space 1}    3.79{col 60}{space 3}0.000{col 68}{space 4} 3.924428{col 81}{space 3} 12.32863
{txt}{space 13}_Icowcode_235 {c |}{col 28}{res}{space 2}-2.038965{col 40}{space 2}  2.14376{col 51}{space 1}   -0.95{col 60}{space 3}0.342{col 68}{space 4}-6.241065{col 81}{space 3} 2.163135
{txt}{space 13}_Icowcode_255 {c |}{col 28}{res}{space 2} .4483276{col 40}{space 2}  2.14376{col 51}{space 1}    0.21{col 60}{space 3}0.834{col 68}{space 4}-3.753773{col 81}{space 3} 4.650428
{txt}{space 13}_Icowcode_290 {c |}{col 28}{res}{space 2}-5.481076{col 40}{space 2}  2.14376{col 51}{space 1}   -2.56{col 60}{space 3}0.011{col 68}{space 4}-9.683176{col 81}{space 3}-1.278975
{txt}{space 13}_Icowcode_305 {c |}{col 28}{res}{space 2} 3.481195{col 40}{space 2} 2.144278{col 51}{space 1}    1.62{col 60}{space 3}0.105{col 68}{space 4}  -.72192{col 81}{space 3} 7.684309
{txt}{space 13}_Icowcode_310 {c |}{col 28}{res}{space 2}-4.912915{col 40}{space 2}  2.13362{col 51}{space 1}   -2.30{col 60}{space 3}0.021{col 68}{space 4} -9.09514{col 81}{space 3}-.7306907
{txt}{space 13}_Icowcode_316 {c |}{col 28}{res}{space 2}-3.248283{col 40}{space 2} 2.136419{col 51}{space 1}   -1.52{col 60}{space 3}0.128{col 68}{space 4}-7.435994{col 81}{space 3} .9394285
{txt}{space 13}_Icowcode_317 {c |}{col 28}{res}{space 2}-5.268701{col 40}{space 2}  2.14376{col 51}{space 1}   -2.46{col 60}{space 3}0.014{col 68}{space 4}-9.470801{col 81}{space 3}-1.066601
{txt}{space 13}_Icowcode_325 {c |}{col 28}{res}{space 2} 11.60589{col 40}{space 2}  2.14376{col 51}{space 1}    5.41{col 60}{space 3}0.000{col 68}{space 4} 7.403787{col 81}{space 3} 15.80799
{txt}{space 13}_Icowcode_331 {c |}{col 28}{res}{space 2} 79.48671{col 40}{space 2}  2.14376{col 51}{space 1}   37.08{col 60}{space 3}0.000{col 68}{space 4} 75.28461{col 81}{space 3} 83.68881
{txt}{space 13}_Icowcode_338 {c |}{col 28}{res}{space 2}-1.281541{col 40}{space 2} 2.127904{col 51}{space 1}   -0.60{col 60}{space 3}0.547{col 68}{space 4}-5.452561{col 81}{space 3} 2.889479
{txt}{space 13}_Icowcode_339 {c |}{col 28}{res}{space 2}-4.361458{col 40}{space 2} 2.147442{col 51}{space 1}   -2.03{col 60}{space 3}0.042{col 68}{space 4}-8.570775{col 81}{space 3}-.1521398
{txt}{space 13}_Icowcode_341 {c |}{col 28}{res}{space 2}-4.443937{col 40}{space 2} 2.153385{col 51}{space 1}   -2.06{col 60}{space 3}0.039{col 68}{space 4}-8.664903{col 81}{space 3}-.2229706
{txt}{space 13}_Icowcode_343 {c |}{col 28}{res}{space 2} -3.87939{col 40}{space 2} 2.147895{col 51}{space 1}   -1.81{col 60}{space 3}0.071{col 68}{space 4}-8.089596{col 81}{space 3}  .330815
{txt}{space 13}_Icowcode_344 {c |}{col 28}{res}{space 2}-3.546797{col 40}{space 2} 2.148372{col 51}{space 1}   -1.65{col 60}{space 3}0.099{col 68}{space 4}-7.757938{col 81}{space 3} .6643444
{txt}{space 13}_Icowcode_346 {c |}{col 28}{res}{space 2}-3.896735{col 40}{space 2} 2.155259{col 51}{space 1}   -1.81{col 60}{space 3}0.071{col 68}{space 4}-8.121375{col 81}{space 3} .3279044
{txt}{space 13}_Icowcode_347 {c |}{col 28}{res}{space 2}-5.417652{col 40}{space 2}  2.14376{col 51}{space 1}   -2.53{col 60}{space 3}0.012{col 68}{space 4}-9.619753{col 81}{space 3}-1.215552
{txt}{space 13}_Icowcode_349 {c |}{col 28}{res}{space 2}-2.259146{col 40}{space 2}  2.14376{col 51}{space 1}   -1.05{col 60}{space 3}0.292{col 68}{space 4}-6.461246{col 81}{space 3} 1.942955
{txt}{space 13}_Icowcode_350 {c |}{col 28}{res}{space 2}-4.787365{col 40}{space 2}  2.14376{col 51}{space 1}   -2.23{col 60}{space 3}0.026{col 68}{space 4}-8.989465{col 81}{space 3}-.5852642
{txt}{space 13}_Icowcode_352 {c |}{col 28}{res}{space 2}-3.476161{col 40}{space 2} 2.147442{col 51}{space 1}   -1.62{col 60}{space 3}0.106{col 68}{space 4}-7.685478{col 81}{space 3} .7331573
{txt}{space 13}_Icowcode_355 {c |}{col 28}{res}{space 2}-3.318399{col 40}{space 2} 2.161344{col 51}{space 1}   -1.54{col 60}{space 3}0.125{col 68}{space 4}-7.554966{col 81}{space 3} .9181681
{txt}{space 13}_Icowcode_359 {c |}{col 28}{res}{space 2}-4.407772{col 40}{space 2} 2.132734{col 51}{space 1}   -2.07{col 60}{space 3}0.039{col 68}{space 4}-8.588258{col 81}{space 3}-.2272853
{txt}{space 13}_Icowcode_360 {c |}{col 28}{res}{space 2}-4.550039{col 40}{space 2} 2.146232{col 51}{space 1}   -2.12{col 60}{space 3}0.034{col 68}{space 4}-8.756985{col 81}{space 3}-.3430924
{txt}{space 13}_Icowcode_365 {c |}{col 28}{res}{space 2}-2.686194{col 40}{space 2} 2.116497{col 51}{space 1}   -1.27{col 60}{space 3}0.204{col 68}{space 4}-6.834854{col 81}{space 3} 1.462467
{txt}{space 13}_Icowcode_366 {c |}{col 28}{res}{space 2}-1.151368{col 40}{space 2}  2.14376{col 51}{space 1}   -0.54{col 60}{space 3}0.591{col 68}{space 4}-5.353468{col 81}{space 3} 3.050732
{txt}{space 13}_Icowcode_367 {c |}{col 28}{res}{space 2}-4.294922{col 40}{space 2}  2.14376{col 51}{space 1}   -2.00{col 60}{space 3}0.045{col 68}{space 4}-8.497023{col 81}{space 3}-.0928218
{txt}{space 13}_Icowcode_368 {c |}{col 28}{res}{space 2}-4.656799{col 40}{space 2}  2.14376{col 51}{space 1}   -2.17{col 60}{space 3}0.030{col 68}{space 4}-8.858899{col 81}{space 3}-.4546986
{txt}{space 13}_Icowcode_369 {c |}{col 28}{res}{space 2}-5.804838{col 40}{space 2}  2.14376{col 51}{space 1}   -2.71{col 60}{space 3}0.007{col 68}{space 4}-10.00694{col 81}{space 3}-1.602737
{txt}{space 13}_Icowcode_370 {c |}{col 28}{res}{space 2}-4.796612{col 40}{space 2} 2.148875{col 51}{space 1}   -2.23{col 60}{space 3}0.026{col 68}{space 4}-9.008737{col 81}{space 3} -.584486
{txt}{space 13}_Icowcode_371 {c |}{col 28}{res}{space 2}-4.271203{col 40}{space 2} 2.138401{col 51}{space 1}   -2.00{col 60}{space 3}0.046{col 68}{space 4}-8.462799{col 81}{space 3}-.0796064
{txt}{space 13}_Icowcode_372 {c |}{col 28}{res}{space 2} -5.57691{col 40}{space 2}  2.14376{col 51}{space 1}   -2.60{col 60}{space 3}0.009{col 68}{space 4}-9.779011{col 81}{space 3} -1.37481
{txt}{space 13}_Icowcode_373 {c |}{col 28}{res}{space 2}-5.523115{col 40}{space 2}  2.14354{col 51}{space 1}   -2.58{col 60}{space 3}0.010{col 68}{space 4}-9.724785{col 81}{space 3}-1.321445
{txt}{space 13}_Icowcode_375 {c |}{col 28}{res}{space 2}-3.684224{col 40}{space 2}  2.14376{col 51}{space 1}   -1.72{col 60}{space 3}0.086{col 68}{space 4}-7.886325{col 81}{space 3} .5178762
{txt}{space 13}_Icowcode_380 {c |}{col 28}{res}{space 2}-2.129319{col 40}{space 2}  2.14376{col 51}{space 1}   -0.99{col 60}{space 3}0.321{col 68}{space 4}-6.331419{col 81}{space 3} 2.072782
{txt}{space 13}_Icowcode_385 {c |}{col 28}{res}{space 2} 2.378586{col 40}{space 2}  2.14376{col 51}{space 1}    1.11{col 60}{space 3}0.267{col 68}{space 4}-1.823514{col 81}{space 3} 6.580686
{txt}{space 13}_Icowcode_390 {c |}{col 28}{res}{space 2}-.5515065{col 40}{space 2} 2.146232{col 51}{space 1}   -0.26{col 60}{space 3}0.797{col 68}{space 4}-4.758453{col 81}{space 3}  3.65544
{txt}{space 13}_Icowcode_395 {c |}{col 28}{res}{space 2} 23.91292{col 40}{space 2} 2.148372{col 51}{space 1}   11.13{col 60}{space 3}0.000{col 68}{space 4} 19.70177{col 81}{space 3} 28.12406
{txt}{space 13}_Icowcode_403 {c |}{col 28}{res}{space 2}-5.850483{col 40}{space 2}  2.14376{col 51}{space 1}   -2.73{col 60}{space 3}0.006{col 68}{space 4}-10.05258{col 81}{space 3}-1.648382
{txt}{space 13}_Icowcode_404 {c |}{col 28}{res}{space 2}-5.843554{col 40}{space 2}  2.14376{col 51}{space 1}   -2.73{col 60}{space 3}0.006{col 68}{space 4}-10.04565{col 81}{space 3}-1.641453
{txt}{space 13}_Icowcode_411 {c |}{col 28}{res}{space 2}-3.637075{col 40}{space 2} 2.125693{col 51}{space 1}   -1.71{col 60}{space 3}0.087{col 68}{space 4}-7.803761{col 81}{space 3} .5296115
{txt}{space 13}_Icowcode_420 {c |}{col 28}{res}{space 2}-5.835294{col 40}{space 2}  2.14376{col 51}{space 1}   -2.72{col 60}{space 3}0.006{col 68}{space 4}-10.03739{col 81}{space 3}-1.633193
{txt}{space 13}_Icowcode_432 {c |}{col 28}{res}{space 2}-5.848554{col 40}{space 2}  2.14376{col 51}{space 1}   -2.73{col 60}{space 3}0.006{col 68}{space 4}-10.05065{col 81}{space 3}-1.646453
{txt}{space 13}_Icowcode_433 {c |}{col 28}{res}{space 2}-5.768295{col 40}{space 2}  2.14376{col 51}{space 1}   -2.69{col 60}{space 3}0.007{col 68}{space 4}-9.970395{col 81}{space 3}-1.566195
{txt}{space 13}_Icowcode_434 {c |}{col 28}{res}{space 2}-5.844263{col 40}{space 2}  2.14376{col 51}{space 1}   -2.73{col 60}{space 3}0.006{col 68}{space 4}-10.04636{col 81}{space 3}-1.642163
{txt}{space 13}_Icowcode_435 {c |}{col 28}{res}{space 2}-4.110212{col 40}{space 2} 2.166132{col 51}{space 1}   -1.90{col 60}{space 3}0.058{col 68}{space 4}-8.356164{col 81}{space 3} .1357398
{txt}{space 13}_Icowcode_436 {c |}{col 28}{res}{space 2}-5.847011{col 40}{space 2}  2.14376{col 51}{space 1}   -2.73{col 60}{space 3}0.006{col 68}{space 4}-10.04911{col 81}{space 3} -1.64491
{txt}{space 13}_Icowcode_437 {c |}{col 28}{res}{space 2}-5.815687{col 40}{space 2}  2.14376{col 51}{space 1}   -2.71{col 60}{space 3}0.007{col 68}{space 4}-10.01779{col 81}{space 3}-1.613587
{txt}{space 13}_Icowcode_438 {c |}{col 28}{res}{space 2}-5.845601{col 40}{space 2}  2.14376{col 51}{space 1}   -2.73{col 60}{space 3}0.006{col 68}{space 4} -10.0477{col 81}{space 3}  -1.6435
{txt}{space 13}_Icowcode_439 {c |}{col 28}{res}{space 2}-5.759526{col 40}{space 2}  2.14376{col 51}{space 1}   -2.69{col 60}{space 3}0.007{col 68}{space 4}-9.961626{col 81}{space 3}-1.557425
{txt}{space 13}_Icowcode_450 {c |}{col 28}{res}{space 2}-4.071785{col 40}{space 2} 2.167196{col 51}{space 1}   -1.88{col 60}{space 3}0.060{col 68}{space 4}-8.319822{col 81}{space 3} .1762528
{txt}{space 13}_Icowcode_451 {c |}{col 28}{res}{space 2}-5.850483{col 40}{space 2}  2.14376{col 51}{space 1}   -2.73{col 60}{space 3}0.006{col 68}{space 4}-10.05258{col 81}{space 3}-1.648382
{txt}{space 13}_Icowcode_452 {c |}{col 28}{res}{space 2}-5.815289{col 40}{space 2}  2.14376{col 51}{space 1}   -2.71{col 60}{space 3}0.007{col 68}{space 4}-10.01739{col 81}{space 3}-1.613188
{txt}{space 13}_Icowcode_461 {c |}{col 28}{res}{space 2}-5.811797{col 40}{space 2}  2.14376{col 51}{space 1}   -2.71{col 60}{space 3}0.007{col 68}{space 4} -10.0139{col 81}{space 3}-1.609697
{txt}{space 13}_Icowcode_471 {c |}{col 28}{res}{space 2}-5.813572{col 40}{space 2}  2.14376{col 51}{space 1}   -2.71{col 60}{space 3}0.007{col 68}{space 4}-10.01567{col 81}{space 3}-1.611472
{txt}{space 13}_Icowcode_475 {c |}{col 28}{res}{space 2}-4.809217{col 40}{space 2} 2.144769{col 51}{space 1}   -2.24{col 60}{space 3}0.025{col 68}{space 4}-9.013296{col 81}{space 3}-.6051379
{txt}{space 13}_Icowcode_481 {c |}{col 28}{res}{space 2} -5.04989{col 40}{space 2}  2.13187{col 51}{space 1}   -2.37{col 60}{space 3}0.018{col 68}{space 4}-9.228685{col 81}{space 3}-.8710961
{txt}{space 13}_Icowcode_482 {c |}{col 28}{res}{space 2}-5.287846{col 40}{space 2} 2.144966{col 51}{space 1}   -2.47{col 60}{space 3}0.014{col 68}{space 4}-9.492311{col 81}{space 3}-1.083381
{txt}{space 13}_Icowcode_483 {c |}{col 28}{res}{space 2}-5.369209{col 40}{space 2} 2.144482{col 51}{space 1}   -2.50{col 60}{space 3}0.012{col 68}{space 4}-9.572725{col 81}{space 3}-1.165693
{txt}{space 13}_Icowcode_484 {c |}{col 28}{res}{space 2}-5.397435{col 40}{space 2} 2.144278{col 51}{space 1}   -2.52{col 60}{space 3}0.012{col 68}{space 4} -9.60055{col 81}{space 3}-1.194321
{txt}{space 13}_Icowcode_490 {c |}{col 28}{res}{space 2}-4.995397{col 40}{space 2} 2.147895{col 51}{space 1}   -2.33{col 60}{space 3}0.020{col 68}{space 4}-9.205603{col 81}{space 3}-.7851921
{txt}{space 13}_Icowcode_500 {c |}{col 28}{res}{space 2}-4.999112{col 40}{space 2} 2.147895{col 51}{space 1}   -2.33{col 60}{space 3}0.020{col 68}{space 4}-9.209318{col 81}{space 3}-.7889072
{txt}{space 13}_Icowcode_501 {c |}{col 28}{res}{space 2}-5.844039{col 40}{space 2}  2.14376{col 51}{space 1}   -2.73{col 60}{space 3}0.006{col 68}{space 4}-10.04614{col 81}{space 3}-1.641938
{txt}{space 13}_Icowcode_510 {c |}{col 28}{res}{space 2}-5.847408{col 40}{space 2}  2.14376{col 51}{space 1}   -2.73{col 60}{space 3}0.006{col 68}{space 4}-10.04951{col 81}{space 3}-1.645308
{txt}{space 13}_Icowcode_516 {c |}{col 28}{res}{space 2}-5.260727{col 40}{space 2} 2.145246{col 51}{space 1}   -2.45{col 60}{space 3}0.014{col 68}{space 4} -9.46574{col 81}{space 3}-1.055715
{txt}{space 13}_Icowcode_517 {c |}{col 28}{res}{space 2}-5.326088{col 40}{space 2} 2.144482{col 51}{space 1}   -2.48{col 60}{space 3}0.013{col 68}{space 4}-9.529604{col 81}{space 3}-1.122572
{txt}{space 13}_Icowcode_520 {c |}{col 28}{res}{space 2} -5.84904{col 40}{space 2}  2.14376{col 51}{space 1}   -2.73{col 60}{space 3}0.006{col 68}{space 4}-10.05114{col 81}{space 3} -1.64694
{txt}{space 13}_Icowcode_522 {c |}{col 28}{res}{space 2}-5.755679{col 40}{space 2}  2.14376{col 51}{space 1}   -2.68{col 60}{space 3}0.007{col 68}{space 4}-9.957779{col 81}{space 3}-1.553579
{txt}{space 13}_Icowcode_525 {c |}{col 28}{res}{space 2}-5.850483{col 40}{space 2}  2.14376{col 51}{space 1}   -2.73{col 60}{space 3}0.006{col 68}{space 4}-10.05258{col 81}{space 3}-1.648382
{txt}{space 13}_Icowcode_530 {c |}{col 28}{res}{space 2}-4.669328{col 40}{space 2}  2.15308{col 51}{space 1}   -2.17{col 60}{space 3}0.030{col 68}{space 4}-8.889697{col 81}{space 3}-.4489578
{txt}{space 13}_Icowcode_531 {c |}{col 28}{res}{space 2}        0{col 40}{txt}  (omitted)
{space 13}_Icowcode_540 {c |}{col 28}{res}{space 2}-4.519465{col 40}{space 2} 2.125153{col 51}{space 1}   -2.13{col 60}{space 3}0.033{col 68}{space 4}-8.685093{col 81}{space 3}-.3538375
{txt}{space 13}_Icowcode_541 {c |}{col 28}{res}{space 2} -4.85351{col 40}{space 2} 2.149953{col 51}{space 1}   -2.26{col 60}{space 3}0.024{col 68}{space 4}-9.067749{col 81}{space 3}  -.63927
{txt}{space 13}_Icowcode_551 {c |}{col 28}{res}{space 2}-4.958241{col 40}{space 2} 2.148372{col 51}{space 1}   -2.31{col 60}{space 3}0.021{col 68}{space 4}-9.169382{col 81}{space 3}-.7470994
{txt}{space 13}_Icowcode_552 {c |}{col 28}{res}{space 2} -4.00405{col 40}{space 2} 2.169396{col 51}{space 1}   -1.85{col 60}{space 3}0.065{col 68}{space 4}-8.256401{col 81}{space 3} .2483009
{txt}{space 13}_Icowcode_553 {c |}{col 28}{res}{space 2}-4.976258{col 40}{space 2} 2.146375{col 51}{space 1}   -2.32{col 60}{space 3}0.020{col 68}{space 4}-9.183484{col 81}{space 3}-.7690319
{txt}{space 13}_Icowcode_560 {c |}{col 28}{res}{space 2}-5.500363{col 40}{space 2} 2.140476{col 51}{space 1}   -2.57{col 60}{space 3}0.010{col 68}{space 4}-9.696026{col 81}{space 3}-1.304701
{txt}{space 13}_Icowcode_565 {c |}{col 28}{res}{space 2}-5.815119{col 40}{space 2}  2.14376{col 51}{space 1}   -2.71{col 60}{space 3}0.007{col 68}{space 4}-10.01722{col 81}{space 3}-1.613018
{txt}{space 13}_Icowcode_570 {c |}{col 28}{res}{space 2}-5.850483{col 40}{space 2}  2.14376{col 51}{space 1}   -2.73{col 60}{space 3}0.006{col 68}{space 4}-10.05258{col 81}{space 3}-1.648382
{txt}{space 13}_Icowcode_571 {c |}{col 28}{res}{space 2}-5.533925{col 40}{space 2} 2.138401{col 51}{space 1}   -2.59{col 60}{space 3}0.010{col 68}{space 4}-9.725521{col 81}{space 3}-1.342329
{txt}{space 13}_Icowcode_580 {c |}{col 28}{res}{space 2} -4.44914{col 40}{space 2} 2.124524{col 51}{space 1}   -2.09{col 60}{space 3}0.036{col 68}{space 4}-8.613534{col 81}{space 3}-.2847451
{txt}{space 13}_Icowcode_581 {c |}{col 28}{res}{space 2}-3.318021{col 40}{space 2} 2.117248{col 51}{space 1}   -1.57{col 60}{space 3}0.117{col 68}{space 4}-7.468153{col 81}{space 3} .8321104
{txt}{space 13}_Icowcode_590 {c |}{col 28}{res}{space 2}-2.224773{col 40}{space 2} 2.116485{col 51}{space 1}   -1.05{col 60}{space 3}0.293{col 68}{space 4} -6.37341{col 81}{space 3} 1.923864
{txt}{space 13}_Icowcode_591 {c |}{col 28}{res}{space 2}-1.778743{col 40}{space 2} 2.116497{col 51}{space 1}   -0.84{col 60}{space 3}0.401{col 68}{space 4}-5.927403{col 81}{space 3} 2.369918
{txt}{space 13}_Icowcode_600 {c |}{col 28}{res}{space 2}-4.957325{col 40}{space 2} 2.147442{col 51}{space 1}   -2.31{col 60}{space 3}0.021{col 68}{space 4}-9.166642{col 81}{space 3}-.7480068
{txt}{space 13}_Icowcode_615 {c |}{col 28}{res}{space 2}-5.752479{col 40}{space 2}  2.14376{col 51}{space 1}   -2.68{col 60}{space 3}0.007{col 68}{space 4} -9.95458{col 81}{space 3}-1.550379
{txt}{space 13}_Icowcode_616 {c |}{col 28}{res}{space 2} -4.37413{col 40}{space 2}  2.14286{col 51}{space 1}   -2.04{col 60}{space 3}0.041{col 68}{space 4}-8.574466{col 81}{space 3}-.1737931
{txt}{space 13}_Icowcode_620 {c |}{col 28}{res}{space 2}-5.849186{col 40}{space 2}  2.14376{col 51}{space 1}   -2.73{col 60}{space 3}0.006{col 68}{space 4}-10.05129{col 81}{space 3}-1.647086
{txt}{space 13}_Icowcode_625 {c |}{col 28}{res}{space 2}-5.279747{col 40}{space 2}  2.13453{col 51}{space 1}   -2.47{col 60}{space 3}0.013{col 68}{space 4}-9.463755{col 81}{space 3}-1.095739
{txt}{space 13}_Icowcode_630 {c |}{col 28}{res}{space 2}-1.117621{col 40}{space 2}  2.14376{col 51}{space 1}   -0.52{col 60}{space 3}0.602{col 68}{space 4}-5.319721{col 81}{space 3} 3.084479
{txt}{space 13}_Icowcode_640 {c |}{col 28}{res}{space 2}-4.135495{col 40}{space 2} 2.125806{col 51}{space 1}   -1.95{col 60}{space 3}0.052{col 68}{space 4}-8.302401{col 81}{space 3} .0314118
{txt}{space 13}_Icowcode_645 {c |}{col 28}{res}{space 2}-2.686785{col 40}{space 2} 2.116485{col 51}{space 1}   -1.27{col 60}{space 3}0.204{col 68}{space 4}-6.835422{col 81}{space 3} 1.461853
{txt}{space 13}_Icowcode_651 {c |}{col 28}{res}{space 2}-5.787234{col 40}{space 2}  2.14376{col 51}{space 1}   -2.70{col 60}{space 3}0.007{col 68}{space 4}-9.989335{col 81}{space 3}-1.585134
{txt}{space 13}_Icowcode_652 {c |}{col 28}{res}{space 2}-5.848178{col 40}{space 2}  2.14376{col 51}{space 1}   -2.73{col 60}{space 3}0.006{col 68}{space 4}-10.05028{col 81}{space 3}-1.646078
{txt}{space 13}_Icowcode_660 {c |}{col 28}{res}{space 2}-3.498152{col 40}{space 2} 2.132462{col 51}{space 1}   -1.64{col 60}{space 3}0.101{col 68}{space 4}-7.678107{col 81}{space 3} .6818023
{txt}{space 13}_Icowcode_663 {c |}{col 28}{res}{space 2}-3.902576{col 40}{space 2} 2.121733{col 51}{space 1}   -1.84{col 60}{space 3}0.066{col 68}{space 4}  -8.0615{col 81}{space 3} .2563475
{txt}{space 13}_Icowcode_666 {c |}{col 28}{res}{space 2} .4536949{col 40}{space 2} 2.116485{col 51}{space 1}    0.21{col 60}{space 3}0.830{col 68}{space 4}-3.694942{col 81}{space 3} 4.602332
{txt}{space 13}_Icowcode_670 {c |}{col 28}{res}{space 2}-4.057245{col 40}{space 2} 2.123919{col 51}{space 1}   -1.91{col 60}{space 3}0.056{col 68}{space 4}-8.220453{col 81}{space 3} .1059626
{txt}{space 13}_Icowcode_679 {c |}{col 28}{res}{space 2}-5.850483{col 40}{space 2}  2.14376{col 51}{space 1}   -2.73{col 60}{space 3}0.006{col 68}{space 4}-10.05258{col 81}{space 3}-1.648382
{txt}{space 13}_Icowcode_690 {c |}{col 28}{res}{space 2} -2.34699{col 40}{space 2} 2.116676{col 51}{space 1}   -1.11{col 60}{space 3}0.268{col 68}{space 4}-6.496001{col 81}{space 3}  1.80202
{txt}{space 13}_Icowcode_692 {c |}{col 28}{res}{space 2} .2861041{col 40}{space 2} 2.117926{col 51}{space 1}    0.14{col 60}{space 3}0.893{col 68}{space 4}-3.865358{col 81}{space 3} 4.437566
{txt}{space 13}_Icowcode_694 {c |}{col 28}{res}{space 2}-1.329083{col 40}{space 2} 2.137679{col 51}{space 1}   -0.62{col 60}{space 3}0.534{col 68}{space 4}-5.519262{col 81}{space 3} 2.861097
{txt}{space 13}_Icowcode_696 {c |}{col 28}{res}{space 2}-3.782088{col 40}{space 2} 2.164076{col 51}{space 1}   -1.75{col 60}{space 3}0.081{col 68}{space 4}-8.024009{col 81}{space 3} .4598341
{txt}{space 13}_Icowcode_698 {c |}{col 28}{res}{space 2}-3.728243{col 40}{space 2} 2.138647{col 51}{space 1}   -1.74{col 60}{space 3}0.081{col 68}{space 4} -7.92032{col 81}{space 3} .4638345
{txt}{space 13}_Icowcode_700 {c |}{col 28}{res}{space 2}-2.598594{col 40}{space 2} 2.116592{col 51}{space 1}   -1.23{col 60}{space 3}0.220{col 68}{space 4}-6.747441{col 81}{space 3} 1.550253
{txt}{space 13}_Icowcode_701 {c |}{col 28}{res}{space 2}-5.850483{col 40}{space 2}  2.14376{col 51}{space 1}   -2.73{col 60}{space 3}0.006{col 68}{space 4}-10.05258{col 81}{space 3}-1.648382
{txt}{space 13}_Icowcode_702 {c |}{col 28}{res}{space 2}-3.789383{col 40}{space 2} 2.133335{col 51}{space 1}   -1.78{col 60}{space 3}0.076{col 68}{space 4}-7.971049{col 81}{space 3} .3922839
{txt}{space 13}_Icowcode_703 {c |}{col 28}{res}{space 2}-5.771061{col 40}{space 2}  2.14376{col 51}{space 1}   -2.69{col 60}{space 3}0.007{col 68}{space 4}-9.973162{col 81}{space 3}-1.568961
{txt}{space 13}_Icowcode_704 {c |}{col 28}{res}{space 2}-4.643508{col 40}{space 2}  2.13959{col 51}{space 1}   -2.17{col 60}{space 3}0.030{col 68}{space 4}-8.837434{col 81}{space 3}-.4495812
{txt}{space 13}_Icowcode_705 {c |}{col 28}{res}{space 2} -4.83092{col 40}{space 2}  2.12942{col 51}{space 1}   -2.27{col 60}{space 3}0.023{col 68}{space 4}-9.004912{col 81}{space 3}-.6569275
{txt}{space 13}_Icowcode_710 {c |}{col 28}{res}{space 2}-4.967309{col 40}{space 2}  2.14376{col 51}{space 1}   -2.32{col 60}{space 3}0.021{col 68}{space 4} -9.16941{col 81}{space 3}-.7652091
{txt}{space 13}_Icowcode_712 {c |}{col 28}{res}{space 2}-2.699058{col 40}{space 2} 2.116497{col 51}{space 1}   -1.28{col 60}{space 3}0.202{col 68}{space 4}-6.847719{col 81}{space 3} 1.449602
{txt}{space 13}_Icowcode_713 {c |}{col 28}{res}{space 2}        0{col 40}{txt}  (omitted)
{space 13}_Icowcode_715 {c |}{col 28}{res}{space 2} -5.06207{col 40}{space 2}  2.14376{col 51}{space 1}   -2.36{col 60}{space 3}0.018{col 68}{space 4}-9.264171{col 81}{space 3}  -.85997
{txt}{space 13}_Icowcode_716 {c |}{col 28}{res}{space 2}-5.216521{col 40}{space 2}  2.14376{col 51}{space 1}   -2.43{col 60}{space 3}0.015{col 68}{space 4}-9.418621{col 81}{space 3}-1.014421
{txt}{space 13}_Icowcode_731 {c |}{col 28}{res}{space 2}-5.850483{col 40}{space 2}  2.14376{col 51}{space 1}   -2.73{col 60}{space 3}0.006{col 68}{space 4}-10.05258{col 81}{space 3}-1.648382
{txt}{space 13}_Icowcode_732 {c |}{col 28}{res}{space 2}-1.845242{col 40}{space 2} 2.302937{col 51}{space 1}   -0.80{col 60}{space 3}0.423{col 68}{space 4}-6.359353{col 81}{space 3} 2.668869
{txt}{space 13}_Icowcode_740 {c |}{col 28}{res}{space 2}-3.067314{col 40}{space 2} 2.311563{col 51}{space 1}   -1.33{col 60}{space 3}0.185{col 68}{space 4}-7.598333{col 81}{space 3} 1.463705
{txt}{space 13}_Icowcode_750 {c |}{col 28}{res}{space 2}-4.959398{col 40}{space 2} 2.148372{col 51}{space 1}   -2.31{col 60}{space 3}0.021{col 68}{space 4}-9.170539{col 81}{space 3}-.7482565
{txt}{space 13}_Icowcode_760 {c |}{col 28}{res}{space 2}-5.807445{col 40}{space 2}  2.14376{col 51}{space 1}   -2.71{col 60}{space 3}0.007{col 68}{space 4}-10.00955{col 81}{space 3}-1.605344
{txt}{space 13}_Icowcode_770 {c |}{col 28}{res}{space 2}-5.771385{col 40}{space 2}  2.14376{col 51}{space 1}   -2.69{col 60}{space 3}0.007{col 68}{space 4}-9.973485{col 81}{space 3}-1.569285
{txt}{space 13}_Icowcode_771 {c |}{col 28}{res}{space 2}-5.478465{col 40}{space 2} 2.143943{col 51}{space 1}   -2.56{col 60}{space 3}0.011{col 68}{space 4}-9.680923{col 81}{space 3}-1.276007
{txt}{space 13}_Icowcode_775 {c |}{col 28}{res}{space 2}-2.402393{col 40}{space 2} 2.131812{col 51}{space 1}   -1.13{col 60}{space 3}0.260{col 68}{space 4}-6.581073{col 81}{space 3} 1.776288
{txt}{space 13}_Icowcode_780 {c |}{col 28}{res}{space 2} -5.78336{col 40}{space 2}  2.14376{col 51}{space 1}   -2.70{col 60}{space 3}0.007{col 68}{space 4} -9.98546{col 81}{space 3} -1.58126
{txt}{space 13}_Icowcode_781 {c |}{col 28}{res}{space 2}-3.936465{col 40}{space 2} 2.123337{col 51}{space 1}   -1.85{col 60}{space 3}0.064{col 68}{space 4}-8.098532{col 81}{space 3} .2256027
{txt}{space 13}_Icowcode_790 {c |}{col 28}{res}{space 2} -4.64633{col 40}{space 2} 2.126482{col 51}{space 1}   -2.18{col 60}{space 3}0.029{col 68}{space 4}-8.814562{col 81}{space 3}-.4780987
{txt}{space 13}_Icowcode_800 {c |}{col 28}{res}{space 2}-4.678409{col 40}{space 2} 2.149953{col 51}{space 1}   -2.18{col 60}{space 3}0.030{col 68}{space 4}-8.892649{col 81}{space 3}-.4641698
{txt}{space 13}_Icowcode_811 {c |}{col 28}{res}{space 2}-5.769762{col 40}{space 2}  2.14376{col 51}{space 1}   -2.69{col 60}{space 3}0.007{col 68}{space 4}-9.971863{col 81}{space 3}-1.567662
{txt}{space 13}_Icowcode_812 {c |}{col 28}{res}{space 2}-5.845272{col 40}{space 2}  2.14376{col 51}{space 1}   -2.73{col 60}{space 3}0.006{col 68}{space 4}-10.04737{col 81}{space 3}-1.643171
{txt}{space 13}_Icowcode_817 {c |}{col 28}{res}{space 2}-3.762881{col 40}{space 2} 2.128964{col 51}{space 1}   -1.77{col 60}{space 3}0.077{col 68}{space 4}-7.935979{col 81}{space 3} .4102169
{txt}{space 13}_Icowcode_820 {c |}{col 28}{res}{space 2}-.7535651{col 40}{space 2} 2.334542{col 51}{space 1}   -0.32{col 60}{space 3}0.747{col 68}{space 4}-5.329627{col 81}{space 3} 3.822497
{txt}{space 13}_Icowcode_830 {c |}{col 28}{res}{space 2}-1.022814{col 40}{space 2} 2.116676{col 51}{space 1}   -0.48{col 60}{space 3}0.629{col 68}{space 4}-5.171825{col 81}{space 3} 3.126196
{txt}{space 13}_Icowcode_835 {c |}{col 28}{res}{space 2}-.2483599{col 40}{space 2} 2.329986{col 51}{space 1}   -0.11{col 60}{space 3}0.915{col 68}{space 4}-4.815492{col 81}{space 3} 4.318773
{txt}{space 13}_Icowcode_840 {c |}{col 28}{res}{space 2}-2.353585{col 40}{space 2} 2.116914{col 51}{space 1}   -1.11{col 60}{space 3}0.266{col 68}{space 4}-6.503063{col 81}{space 3} 1.795893
{txt}{space 13}_Icowcode_850 {c |}{col 28}{res}{space 2}-2.771435{col 40}{space 2} 2.116485{col 51}{space 1}   -1.31{col 60}{space 3}0.190{col 68}{space 4}-6.920072{col 81}{space 3} 1.377202
{txt}{space 13}_Icowcode_860 {c |}{col 28}{res}{space 2}-1.158644{col 40}{space 2} 2.145151{col 51}{space 1}   -0.54{col 60}{space 3}0.589{col 68}{space 4}-5.363471{col 81}{space 3} 3.046182
{txt}{space 13}_Icowcode_900 {c |}{col 28}{res}{space 2}-1.477814{col 40}{space 2} 2.116497{col 51}{space 1}   -0.70{col 60}{space 3}0.485{col 68}{space 4}-5.626474{col 81}{space 3} 2.670847
{txt}{space 13}_Icowcode_910 {c |}{col 28}{res}{space 2}-2.033697{col 40}{space 2}  2.35892{col 51}{space 1}   -0.86{col 60}{space 3}0.389{col 68}{space 4}-6.657544{col 81}{space 3} 2.590149
{txt}{space 13}_Icowcode_920 {c |}{col 28}{res}{space 2}-2.245029{col 40}{space 2} 2.116485{col 51}{space 1}   -1.06{col 60}{space 3}0.289{col 68}{space 4}-6.393667{col 81}{space 3} 1.903608
{txt}{space 13}_Icowcode_935 {c |}{col 28}{res}{space 2} -3.25471{col 40}{space 2} 2.117069{col 51}{space 1}   -1.54{col 60}{space 3}0.124{col 68}{space 4}-7.404491{col 81}{space 3} .8950718
{txt}{space 13}_Icowcode_940 {c |}{col 28}{res}{space 2}-4.267694{col 40}{space 2} 2.122779{col 51}{space 1}   -2.01{col 60}{space 3}0.044{col 68}{space 4}-8.428667{col 81}{space 3} -.106721
{txt}{space 13}_Icowcode_946 {c |}{col 28}{res}{space 2}-5.850483{col 40}{space 2}  2.14376{col 51}{space 1}   -2.73{col 60}{space 3}0.006{col 68}{space 4}-10.05258{col 81}{space 3}-1.648382
{txt}{space 13}_Icowcode_947 {c |}{col 28}{res}{space 2} -2.87484{col 40}{space 2} 2.116497{col 51}{space 1}   -1.36{col 60}{space 3}0.174{col 68}{space 4}-7.023501{col 81}{space 3}  1.27382
{txt}{space 13}_Icowcode_950 {c |}{col 28}{res}{space 2} -2.75028{col 40}{space 2} 2.116485{col 51}{space 1}   -1.30{col 60}{space 3}0.194{col 68}{space 4}-6.898917{col 81}{space 3} 1.398357
{txt}{space 13}_Icowcode_955 {c |}{col 28}{res}{space 2} -2.87484{col 40}{space 2} 2.116497{col 51}{space 1}   -1.36{col 60}{space 3}0.174{col 68}{space 4}-7.023501{col 81}{space 3}  1.27382
{txt}{space 13}_Icowcode_970 {c |}{col 28}{res}{space 2}-4.204383{col 40}{space 2} 2.122244{col 51}{space 1}   -1.98{col 60}{space 3}0.048{col 68}{space 4}-8.364308{col 81}{space 3}-.0444574
{txt}{space 13}_Icowcode_983 {c |}{col 28}{res}{space 2}-2.241725{col 40}{space 2}  2.11745{col 51}{space 1}   -1.06{col 60}{space 3}0.290{col 68}{space 4}-6.392254{col 81}{space 3} 1.908803
{txt}{space 13}_Icowcode_986 {c |}{col 28}{res}{space 2}-2.748217{col 40}{space 2} 2.116497{col 51}{space 1}   -1.30{col 60}{space 3}0.194{col 68}{space 4}-6.896878{col 81}{space 3} 1.400443
{txt}{space 13}_Icowcode_987 {c |}{col 28}{res}{space 2}-4.892131{col 40}{space 2} 2.149402{col 51}{space 1}   -2.28{col 60}{space 3}0.023{col 68}{space 4}-9.105289{col 81}{space 3} -.678972
{txt}{space 13}_Icowcode_990 {c |}{col 28}{res}{space 2}-3.887825{col 40}{space 2} 2.119926{col 51}{space 1}   -1.83{col 60}{space 3}0.067{col 68}{space 4}-8.043206{col 81}{space 3} .2675561
{txt}{space 12}_Icowcode_1013 {c |}{col 28}{res}{space 2}-2.558283{col 40}{space 2} 2.116676{col 51}{space 1}   -1.21{col 60}{space 3}0.227{col 68}{space 4}-6.707294{col 81}{space 3} 1.590728
{txt}{space 12}_Icowcode_1015 {c |}{col 28}{res}{space 2}-4.008831{col 40}{space 2} 2.156838{col 51}{space 1}   -1.86{col 60}{space 3}0.063{col 68}{space 4}-8.236567{col 81}{space 3} .2189041
{txt}{space 12}_Icowcode_1020 {c |}{col 28}{res}{space 2}-4.116881{col 40}{space 2} 2.123337{col 51}{space 1}   -1.94{col 60}{space 3}0.053{col 68}{space 4}-8.278948{col 81}{space 3} .0451865
{txt}{space 12}_Icowcode_1027 {c |}{col 28}{res}{space 2}        0{col 40}{txt}  (omitted)
{space 21}_cons {c |}{col 28}{res}{space 2}-.6813267{col 40}{space 2} 1.775444{col 51}{space 1}   -0.38{col 60}{space 3}0.701{col 68}{space 4}-4.161471{col 81}{space 3} 2.798817
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est1{txt} stored)

{com}. eststo: xi: regress confirmed_7_day_pc L14be_cat_1_China_carryover L14be_cat_2_China_carryover L14be_cat_3_China_carryover  time time_sq time_cube i.cowcode
{txt}i.cowcode{col 19}_Icowcode_2-1027{col 39}(naturally coded; _Icowcode_2 omitted)
note: _Icowcode_531 omitted because of collinearity
note: _Icowcode_713 omitted because of collinearity
note: _Icowcode_1027 omitted because of collinearity

      Source {c |}       SS           df       MS      Number of obs   ={res}    11,328
{txt}{hline 13}{c +}{hline 34}   F(197, 11130)   = {res}    28.19
{txt}       Model {c |} {res} 882029.164       197   4477.3054   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 1767781.14    11,130  158.830291   {txt}R-squared       ={res}    0.3329
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.3211
{txt}       Total {c |} {res} 2649810.31    11,327  233.937522   {txt}Root MSE        =   {res} 12.603

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         confirmed_7_day_pc{col 29}{c |}      Coef.{col 41}   Std. Err.{col 53}      t{col 61}   P>|t|{col 69}     [95% Con{col 82}f. Interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
L14be_cat_1_China_carryover {c |}{col 29}{res}{space 2}-4.256193{col 41}{space 2} .5403885{col 52}{space 1}   -7.88{col 61}{space 3}0.000{col 69}{space 4} -5.31545{col 82}{space 3}-3.196935
{txt}L14be_cat_2_China_carryover {c |}{col 29}{res}{space 2} -1.27517{col 41}{space 2} 1.466498{col 52}{space 1}   -0.87{col 61}{space 3}0.385{col 69}{space 4}-4.149766{col 82}{space 3} 1.599425
{txt}L14be_cat_3_China_carryover {c |}{col 29}{res}{space 2}-2.646361{col 41}{space 2}  .568878{col 52}{space 1}   -4.65{col 61}{space 3}0.000{col 69}{space 4}-3.761463{col 82}{space 3}-1.531259
{txt}{space 23}time {c |}{col 29}{res}{space 2} .7469215{col 41}{space 2} .1669404{col 52}{space 1}    4.47{col 61}{space 3}0.000{col 69}{space 4} .4196887{col 82}{space 3} 1.074154
{txt}{space 20}time_sq {c |}{col 29}{res}{space 2}-.0229938{col 41}{space 2} .0040645{col 52}{space 1}   -5.66{col 61}{space 3}0.000{col 69}{space 4}-.0309609{col 82}{space 3}-.0150268
{txt}{space 18}time_cube {c |}{col 29}{res}{space 2} .0002331{col 41}{space 2} .0000306{col 52}{space 1}    7.62{col 61}{space 3}0.000{col 69}{space 4} .0001731{col 82}{space 3} .0002931
{txt}{space 15}_Icowcode_20 {c |}{col 29}{res}{space 2}-4.822325{col 41}{space 2} 2.350552{col 52}{space 1}   -2.05{col 61}{space 3}0.040{col 69}{space 4}-9.429824{col 82}{space 3}-.2148261
{txt}{space 15}_Icowcode_31 {c |}{col 29}{res}{space 2}-2.671102{col 41}{space 2} 2.320524{col 52}{space 1}   -1.15{col 61}{space 3}0.250{col 69}{space 4}-7.219741{col 82}{space 3} 1.877536
{txt}{space 15}_Icowcode_40 {c |}{col 29}{res}{space 2}-6.028321{col 41}{space 2} 2.350552{col 52}{space 1}   -2.56{col 61}{space 3}0.010{col 69}{space 4}-10.63582{col 82}{space 3}-1.420822
{txt}{space 15}_Icowcode_41 {c |}{col 29}{res}{space 2}-5.733018{col 41}{space 2} 2.343672{col 52}{space 1}   -2.45{col 61}{space 3}0.014{col 69}{space 4}-10.32703{col 82}{space 3}-1.139006
{txt}{space 15}_Icowcode_42 {c |}{col 29}{res}{space 2}-5.571312{col 41}{space 2} 2.350552{col 52}{space 1}   -2.37{col 61}{space 3}0.018{col 69}{space 4}-10.17881{col 82}{space 3}-.9638127
{txt}{space 15}_Icowcode_51 {c |}{col 29}{res}{space 2}-2.874439{col 41}{space 2} 2.320434{col 52}{space 1}   -1.24{col 61}{space 3}0.215{col 69}{space 4}  -7.4229{col 82}{space 3} 1.674022
{txt}{space 15}_Icowcode_52 {c |}{col 29}{res}{space 2} -4.25505{col 41}{space 2} 2.331632{col 52}{space 1}   -1.82{col 61}{space 3}0.068{col 69}{space 4}-8.825462{col 82}{space 3} .3153624
{txt}{space 15}_Icowcode_53 {c |}{col 29}{res}{space 2}-5.041505{col 41}{space 2} 2.350346{col 52}{space 1}   -2.15{col 61}{space 3}0.032{col 69}{space 4}  -9.6486{col 82}{space 3}-.4344095
{txt}{space 15}_Icowcode_54 {c |}{col 29}{res}{space 2}-2.800284{col 41}{space 2} 2.388964{col 52}{space 1}   -1.17{col 61}{space 3}0.241{col 69}{space 4}-7.483077{col 82}{space 3} 1.882508
{txt}{space 15}_Icowcode_55 {c |}{col 29}{res}{space 2}-2.439924{col 41}{space 2} 2.321343{col 52}{space 1}   -1.05{col 61}{space 3}0.293{col 69}{space 4}-6.990168{col 82}{space 3} 2.110319
{txt}{space 15}_Icowcode_56 {c |}{col 29}{res}{space 2} -2.36349{col 41}{space 2} 2.324831{col 52}{space 1}   -1.02{col 61}{space 3}0.309{col 69}{space 4}-6.920572{col 82}{space 3} 2.193591
{txt}{space 15}_Icowcode_58 {c |}{col 29}{res}{space 2}-2.371991{col 41}{space 2} 2.320434{col 52}{space 1}   -1.02{col 61}{space 3}0.307{col 69}{space 4}-6.920452{col 82}{space 3} 2.176471
{txt}{space 15}_Icowcode_60 {c |}{col 29}{res}{space 2}-3.941755{col 41}{space 2} 2.379278{col 52}{space 1}   -1.66{col 61}{space 3}0.098{col 69}{space 4}-8.605562{col 82}{space 3} .7220515
{txt}{space 15}_Icowcode_70 {c |}{col 29}{res}{space 2}-5.414899{col 41}{space 2} 2.352433{col 52}{space 1}   -2.30{col 61}{space 3}0.021{col 69}{space 4}-10.02608{col 82}{space 3}-.8037129
{txt}{space 15}_Icowcode_80 {c |}{col 29}{res}{space 2}-3.527211{col 41}{space 2} 2.321012{col 52}{space 1}   -1.52{col 61}{space 3}0.129{col 69}{space 4}-8.076806{col 82}{space 3} 1.022384
{txt}{space 15}_Icowcode_90 {c |}{col 29}{res}{space 2}-2.979048{col 41}{space 2} 2.320434{col 52}{space 1}   -1.28{col 61}{space 3}0.199{col 69}{space 4}-7.527509{col 82}{space 3} 1.569414
{txt}{space 15}_Icowcode_91 {c |}{col 29}{res}{space 2} -4.85955{col 41}{space 2} 2.355038{col 52}{space 1}   -2.06{col 61}{space 3}0.039{col 69}{space 4}-9.475841{col 82}{space 3} -.243259
{txt}{space 15}_Icowcode_92 {c |}{col 29}{res}{space 2}-2.982189{col 41}{space 2} 2.320434{col 52}{space 1}   -1.29{col 61}{space 3}0.199{col 69}{space 4} -7.53065{col 82}{space 3} 1.566273
{txt}{space 15}_Icowcode_93 {c |}{col 29}{res}{space 2}-6.097395{col 41}{space 2} 2.350552{col 52}{space 1}   -2.59{col 61}{space 3}0.009{col 69}{space 4}-10.70489{col 82}{space 3}-1.489896
{txt}{space 15}_Icowcode_94 {c |}{col 29}{res}{space 2}-4.854279{col 41}{space 2} 2.352433{col 52}{space 1}   -2.06{col 61}{space 3}0.039{col 69}{space 4}-9.465465{col 82}{space 3}-.2430933
{txt}{space 15}_Icowcode_95 {c |}{col 29}{res}{space 2}-2.595282{col 41}{space 2} 2.377797{col 52}{space 1}   -1.09{col 61}{space 3}0.275{col 69}{space 4}-7.256185{col 82}{space 3} 2.065621
{txt}{space 14}_Icowcode_100 {c |}{col 29}{res}{space 2}-5.976717{col 41}{space 2} 2.350552{col 52}{space 1}   -2.54{col 61}{space 3}0.011{col 69}{space 4}-10.58422{col 82}{space 3}-1.369218
{txt}{space 14}_Icowcode_101 {c |}{col 29}{res}{space 2}-5.334156{col 41}{space 2} 2.353334{col 52}{space 1}   -2.27{col 61}{space 3}0.023{col 69}{space 4}-9.947107{col 82}{space 3}-.7212044
{txt}{space 14}_Icowcode_110 {c |}{col 29}{res}{space 2}-5.807789{col 41}{space 2} 2.350346{col 52}{space 1}   -2.47{col 61}{space 3}0.013{col 69}{space 4}-10.41488{col 82}{space 3}-1.200693
{txt}{space 14}_Icowcode_115 {c |}{col 29}{res}{space 2}-3.172193{col 41}{space 2} 2.320524{col 52}{space 1}   -1.37{col 61}{space 3}0.172{col 69}{space 4}-7.720832{col 82}{space 3} 1.376445
{txt}{space 14}_Icowcode_130 {c |}{col 29}{res}{space 2}-4.362775{col 41}{space 2} 2.352864{col 52}{space 1}   -1.85{col 61}{space 3}0.064{col 69}{space 4}-8.974805{col 82}{space 3} .2492555
{txt}{space 14}_Icowcode_135 {c |}{col 29}{res}{space 2}-5.861409{col 41}{space 2} 2.350552{col 52}{space 1}   -2.49{col 61}{space 3}0.013{col 69}{space 4}-10.46891{col 82}{space 3}-1.253911
{txt}{space 14}_Icowcode_140 {c |}{col 29}{res}{space 2}-5.921227{col 41}{space 2} 2.350552{col 52}{space 1}   -2.52{col 61}{space 3}0.012{col 69}{space 4}-10.52873{col 82}{space 3}-1.313728
{txt}{space 14}_Icowcode_145 {c |}{col 29}{res}{space 2}-6.052519{col 41}{space 2} 2.350552{col 52}{space 1}   -2.57{col 61}{space 3}0.010{col 69}{space 4}-10.66002{col 82}{space 3} -1.44502
{txt}{space 14}_Icowcode_150 {c |}{col 29}{res}{space 2} -5.30875{col 41}{space 2} 2.353334{col 52}{space 1}   -2.26{col 61}{space 3}0.024{col 69}{space 4}-9.921701{col 82}{space 3}-.6957982
{txt}{space 14}_Icowcode_155 {c |}{col 29}{res}{space 2}-5.161479{col 41}{space 2} 2.350552{col 52}{space 1}   -2.20{col 61}{space 3}0.028{col 69}{space 4}-9.768977{col 82}{space 3}-.5539796
{txt}{space 14}_Icowcode_160 {c |}{col 29}{res}{space 2}-5.827134{col 41}{space 2} 2.350303{col 52}{space 1}   -2.48{col 61}{space 3}0.013{col 69}{space 4}-10.43414{col 82}{space 3}-1.220124
{txt}{space 14}_Icowcode_165 {c |}{col 29}{res}{space 2}-5.093978{col 41}{space 2}  2.35043{col 52}{space 1}   -2.17{col 61}{space 3}0.030{col 69}{space 4}-9.701236{col 82}{space 3}-.4867195
{txt}{space 14}_Icowcode_200 {c |}{col 29}{res}{space 2}-.4244562{col 41}{space 2} 2.372254{col 52}{space 1}   -0.18{col 61}{space 3}0.858{col 69}{space 4}-5.074495{col 82}{space 3} 4.225583
{txt}{space 14}_Icowcode_205 {c |}{col 29}{res}{space 2}-1.302614{col 41}{space 2} 2.350552{col 52}{space 1}   -0.55{col 61}{space 3}0.579{col 69}{space 4}-5.910113{col 82}{space 3} 3.304885
{txt}{space 14}_Icowcode_210 {c |}{col 29}{res}{space 2}-.3760753{col 41}{space 2} 2.350552{col 52}{space 1}   -0.16{col 61}{space 3}0.873{col 69}{space 4}-4.983574{col 82}{space 3} 4.231424
{txt}{space 14}_Icowcode_211 {c |}{col 29}{res}{space 2} .8191014{col 41}{space 2} 2.350552{col 52}{space 1}    0.35{col 61}{space 3}0.727{col 69}{space 4}-3.788397{col 82}{space 3}   5.4266
{txt}{space 14}_Icowcode_212 {c |}{col 29}{res}{space 2} 25.09417{col 41}{space 2} 2.350552{col 52}{space 1}   10.68{col 61}{space 3}0.000{col 69}{space 4} 20.48668{col 82}{space 3} 29.70167
{txt}{space 14}_Icowcode_220 {c |}{col 29}{res}{space 2}-.2465296{col 41}{space 2} 2.350552{col 52}{space 1}   -0.10{col 61}{space 3}0.916{col 69}{space 4}-4.854028{col 82}{space 3} 4.360969
{txt}{space 14}_Icowcode_223 {c |}{col 29}{res}{space 2} 15.54925{col 41}{space 2} 2.350552{col 52}{space 1}    6.62{col 61}{space 3}0.000{col 69}{space 4} 10.94176{col 82}{space 3} 20.15675
{txt}{space 14}_Icowcode_225 {c |}{col 29}{res}{space 2} 13.33926{col 41}{space 2} 2.350552{col 52}{space 1}    5.67{col 61}{space 3}0.000{col 69}{space 4} 8.731765{col 82}{space 3} 17.94676
{txt}{space 14}_Icowcode_230 {c |}{col 29}{res}{space 2} 9.532663{col 41}{space 2} 2.350552{col 52}{space 1}    4.06{col 61}{space 3}0.000{col 69}{space 4} 4.925164{col 82}{space 3} 14.14016
{txt}{space 14}_Icowcode_235 {c |}{col 29}{res}{space 2}-1.838906{col 41}{space 2} 2.350552{col 52}{space 1}   -0.78{col 61}{space 3}0.434{col 69}{space 4}-6.446405{col 82}{space 3} 2.768593
{txt}{space 14}_Icowcode_255 {c |}{col 29}{res}{space 2}  .943343{col 41}{space 2} 2.350552{col 52}{space 1}    0.40{col 61}{space 3}0.688{col 69}{space 4}-3.664156{col 82}{space 3} 5.550842
{txt}{space 14}_Icowcode_290 {c |}{col 29}{res}{space 2}-5.689403{col 41}{space 2} 2.350552{col 52}{space 1}   -2.42{col 61}{space 3}0.016{col 69}{space 4} -10.2969{col 82}{space 3}-1.081904
{txt}{space 14}_Icowcode_305 {c |}{col 29}{res}{space 2} 4.065661{col 41}{space 2} 2.350334{col 52}{space 1}    1.73{col 61}{space 3}0.084{col 69}{space 4}-.5414107{col 82}{space 3} 8.672733
{txt}{space 14}_Icowcode_310 {c |}{col 29}{res}{space 2}-5.545648{col 41}{space 2} 2.346319{col 52}{space 1}   -2.36{col 61}{space 3}0.018{col 69}{space 4}-10.14485{col 82}{space 3}-.9464461
{txt}{space 14}_Icowcode_316 {c |}{col 29}{res}{space 2}-3.687464{col 41}{space 2} 2.350552{col 52}{space 1}   -1.57{col 61}{space 3}0.117{col 69}{space 4}-8.294963{col 82}{space 3} .9200351
{txt}{space 14}_Icowcode_317 {c |}{col 29}{res}{space 2}-5.451831{col 41}{space 2} 2.350552{col 52}{space 1}   -2.32{col 61}{space 3}0.020{col 69}{space 4}-10.05933{col 82}{space 3}-.8443321
{txt}{space 14}_Icowcode_325 {c |}{col 29}{res}{space 2} 13.42483{col 41}{space 2} 2.350552{col 52}{space 1}    5.71{col 61}{space 3}0.000{col 69}{space 4} 8.817328{col 82}{space 3} 18.03233
{txt}{space 14}_Icowcode_331 {c |}{col 29}{res}{space 2}  89.3593{col 41}{space 2} 2.350552{col 52}{space 1}   38.02{col 61}{space 3}0.000{col 69}{space 4} 84.75181{col 82}{space 3}  93.9668
{txt}{space 14}_Icowcode_338 {c |}{col 29}{res}{space 2}-1.474222{col 41}{space 2} 2.337669{col 52}{space 1}   -0.63{col 61}{space 3}0.528{col 69}{space 4}-6.056467{col 82}{space 3} 3.108023
{txt}{space 14}_Icowcode_339 {c |}{col 29}{res}{space 2}-4.671268{col 41}{space 2} 2.352864{col 52}{space 1}   -1.99{col 61}{space 3}0.047{col 69}{space 4}-9.283298{col 82}{space 3}-.0592384
{txt}{space 14}_Icowcode_341 {c |}{col 29}{res}{space 2}-4.802082{col 41}{space 2} 2.352677{col 52}{space 1}   -2.04{col 61}{space 3}0.041{col 69}{space 4}-9.413745{col 82}{space 3}-.1904192
{txt}{space 14}_Icowcode_343 {c |}{col 29}{res}{space 2}-4.128386{col 41}{space 2} 2.353334{col 52}{space 1}   -1.75{col 61}{space 3}0.079{col 69}{space 4}-8.741337{col 82}{space 3} .4845655
{txt}{space 14}_Icowcode_344 {c |}{col 29}{res}{space 2}-3.752712{col 41}{space 2} 2.353843{col 52}{space 1}   -1.59{col 61}{space 3}0.111{col 69}{space 4}-8.366662{col 82}{space 3} .8612386
{txt}{space 14}_Icowcode_346 {c |}{col 29}{res}{space 2} -4.38769{col 41}{space 2}  2.35537{col 52}{space 1}   -1.86{col 61}{space 3}0.063{col 69}{space 4}-9.004632{col 82}{space 3}  .229252
{txt}{space 14}_Icowcode_347 {c |}{col 29}{res}{space 2}-5.618455{col 41}{space 2} 2.350552{col 52}{space 1}   -2.39{col 61}{space 3}0.017{col 69}{space 4}-10.22595{col 82}{space 3}-1.010956
{txt}{space 14}_Icowcode_349 {c |}{col 29}{res}{space 2} -2.08521{col 41}{space 2} 2.350552{col 52}{space 1}   -0.89{col 61}{space 3}0.375{col 69}{space 4}-6.692709{col 82}{space 3} 2.522289
{txt}{space 14}_Icowcode_350 {c |}{col 29}{res}{space 2}-4.913387{col 41}{space 2} 2.350552{col 52}{space 1}   -2.09{col 61}{space 3}0.037{col 69}{space 4}-9.520886{col 82}{space 3} -.305888
{txt}{space 14}_Icowcode_352 {c |}{col 29}{res}{space 2}-3.680936{col 41}{space 2} 2.352864{col 52}{space 1}   -1.56{col 61}{space 3}0.118{col 69}{space 4}-8.292966{col 82}{space 3}  .931094
{txt}{space 14}_Icowcode_355 {c |}{col 29}{res}{space 2}-3.768055{col 41}{space 2} 2.376354{col 52}{space 1}   -1.59{col 61}{space 3}0.113{col 69}{space 4}-8.426129{col 82}{space 3} .8900197
{txt}{space 14}_Icowcode_359 {c |}{col 29}{res}{space 2}-4.979256{col 41}{space 2} 2.344978{col 52}{space 1}   -2.12{col 61}{space 3}0.034{col 69}{space 4}-9.575829{col 82}{space 3}-.3826836
{txt}{space 14}_Icowcode_360 {c |}{col 29}{res}{space 2}-4.893086{col 41}{space 2}  2.35169{col 52}{space 1}   -2.08{col 61}{space 3}0.037{col 69}{space 4}-9.502815{col 82}{space 3}-.2833562
{txt}{space 14}_Icowcode_365 {c |}{col 29}{res}{space 2}-3.003423{col 41}{space 2}  2.32038{col 52}{space 1}   -1.29{col 61}{space 3}0.196{col 69}{space 4}-7.551778{col 82}{space 3} 1.544932
{txt}{space 14}_Icowcode_366 {c |}{col 29}{res}{space 2}-.8460011{col 41}{space 2} 2.350552{col 52}{space 1}   -0.36{col 61}{space 3}0.719{col 69}{space 4}  -5.4535{col 82}{space 3} 3.761498
{txt}{space 14}_Icowcode_367 {c |}{col 29}{res}{space 2}-4.362519{col 41}{space 2} 2.350552{col 52}{space 1}   -1.86{col 61}{space 3}0.063{col 69}{space 4}-8.970018{col 82}{space 3} .2449797
{txt}{space 14}_Icowcode_368 {c |}{col 29}{res}{space 2}-4.767331{col 41}{space 2} 2.350552{col 52}{space 1}   -2.03{col 61}{space 3}0.043{col 69}{space 4}-9.374829{col 82}{space 3}-.1598316
{txt}{space 14}_Icowcode_369 {c |}{col 29}{res}{space 2}-6.051577{col 41}{space 2} 2.350552{col 52}{space 1}   -2.57{col 61}{space 3}0.010{col 69}{space 4}-10.65908{col 82}{space 3}-1.444078
{txt}{space 14}_Icowcode_370 {c |}{col 29}{res}{space 2}-5.147189{col 41}{space 2} 2.354392{col 52}{space 1}   -2.19{col 61}{space 3}0.029{col 69}{space 4}-9.762215{col 82}{space 3}-.5321628
{txt}{space 14}_Icowcode_371 {c |}{col 29}{res}{space 2}-4.690101{col 41}{space 2} 2.350552{col 52}{space 1}   -2.00{col 61}{space 3}0.046{col 69}{space 4}  -9.2976{col 82}{space 3}-.0826021
{txt}{space 14}_Icowcode_372 {c |}{col 29}{res}{space 2}-5.796608{col 41}{space 2} 2.350552{col 52}{space 1}   -2.47{col 61}{space 3}0.014{col 69}{space 4}-10.40411{col 82}{space 3}-1.189109
{txt}{space 14}_Icowcode_373 {c |}{col 29}{res}{space 2}-5.942594{col 41}{space 2} 2.350552{col 52}{space 1}   -2.53{col 61}{space 3}0.011{col 69}{space 4}-10.55009{col 82}{space 3}-1.335095
{txt}{space 14}_Icowcode_375 {c |}{col 29}{res}{space 2}-3.679366{col 41}{space 2} 2.350552{col 52}{space 1}   -1.57{col 61}{space 3}0.118{col 69}{space 4}-8.286864{col 82}{space 3} .9281334
{txt}{space 14}_Icowcode_380 {c |}{col 29}{res}{space 2} -1.93998{col 41}{space 2} 2.350552{col 52}{space 1}   -0.83{col 61}{space 3}0.409{col 69}{space 4}-6.547479{col 82}{space 3} 2.667519
{txt}{space 14}_Icowcode_385 {c |}{col 29}{res}{space 2} 3.102761{col 41}{space 2} 2.350552{col 52}{space 1}    1.32{col 61}{space 3}0.187{col 69}{space 4}-1.504738{col 82}{space 3}  7.71026
{txt}{space 14}_Icowcode_390 {c |}{col 29}{res}{space 2}-.4201512{col 41}{space 2}  2.35169{col 52}{space 1}   -0.18{col 61}{space 3}0.858{col 69}{space 4}-5.029881{col 82}{space 3} 4.189578
{txt}{space 14}_Icowcode_395 {c |}{col 29}{res}{space 2} 26.96493{col 41}{space 2} 2.353843{col 52}{space 1}   11.46{col 61}{space 3}0.000{col 69}{space 4} 22.35098{col 82}{space 3} 31.57888
{txt}{space 14}_Icowcode_403 {c |}{col 29}{res}{space 2}-6.102638{col 41}{space 2} 2.350552{col 52}{space 1}   -2.60{col 61}{space 3}0.009{col 69}{space 4}-10.71014{col 82}{space 3}-1.495139
{txt}{space 14}_Icowcode_404 {c |}{col 29}{res}{space 2}-6.094887{col 41}{space 2} 2.350552{col 52}{space 1}   -2.59{col 61}{space 3}0.010{col 69}{space 4}-10.70239{col 82}{space 3}-1.487388
{txt}{space 14}_Icowcode_411 {c |}{col 29}{res}{space 2}-4.356059{col 41}{space 2} 2.331609{col 52}{space 1}   -1.87{col 61}{space 3}0.062{col 69}{space 4}-8.926426{col 82}{space 3} .2143076
{txt}{space 14}_Icowcode_420 {c |}{col 29}{res}{space 2}-6.085647{col 41}{space 2} 2.350552{col 52}{space 1}   -2.59{col 61}{space 3}0.010{col 69}{space 4}-10.69315{col 82}{space 3}-1.478148
{txt}{space 14}_Icowcode_432 {c |}{col 29}{res}{space 2} -6.10048{col 41}{space 2} 2.350552{col 52}{space 1}   -2.60{col 61}{space 3}0.009{col 69}{space 4}-10.70798{col 82}{space 3}-1.492981
{txt}{space 14}_Icowcode_433 {c |}{col 29}{res}{space 2}-6.010699{col 41}{space 2} 2.350552{col 52}{space 1}   -2.56{col 61}{space 3}0.011{col 69}{space 4} -10.6182{col 82}{space 3}  -1.4032
{txt}{space 14}_Icowcode_434 {c |}{col 29}{res}{space 2} -6.09568{col 41}{space 2} 2.350552{col 52}{space 1}   -2.59{col 61}{space 3}0.010{col 69}{space 4}-10.70318{col 82}{space 3}-1.488182
{txt}{space 14}_Icowcode_435 {c |}{col 29}{res}{space 2}-4.299696{col 41}{space 2} 2.376354{col 52}{space 1}   -1.81{col 61}{space 3}0.070{col 69}{space 4}-8.957771{col 82}{space 3} .3583779
{txt}{space 14}_Icowcode_436 {c |}{col 29}{res}{space 2}-6.098754{col 41}{space 2} 2.350552{col 52}{space 1}   -2.59{col 61}{space 3}0.009{col 69}{space 4}-10.70625{col 82}{space 3}-1.491255
{txt}{space 14}_Icowcode_437 {c |}{col 29}{res}{space 2}-6.063714{col 41}{space 2} 2.350552{col 52}{space 1}   -2.58{col 61}{space 3}0.010{col 69}{space 4}-10.67121{col 82}{space 3}-1.456215
{txt}{space 14}_Icowcode_438 {c |}{col 29}{res}{space 2}-6.097176{col 41}{space 2} 2.350552{col 52}{space 1}   -2.59{col 61}{space 3}0.010{col 69}{space 4}-10.70468{col 82}{space 3}-1.489678
{txt}{space 14}_Icowcode_439 {c |}{col 29}{res}{space 2}-6.000889{col 41}{space 2} 2.350552{col 52}{space 1}   -2.55{col 61}{space 3}0.011{col 69}{space 4}-10.60839{col 82}{space 3} -1.39339
{txt}{space 14}_Icowcode_450 {c |}{col 29}{res}{space 2} -4.25309{col 41}{space 2} 2.377797{col 52}{space 1}   -1.79{col 61}{space 3}0.074{col 69}{space 4}-8.913993{col 82}{space 3} .4078136
{txt}{space 14}_Icowcode_451 {c |}{col 29}{res}{space 2}-6.102638{col 41}{space 2} 2.350552{col 52}{space 1}   -2.60{col 61}{space 3}0.009{col 69}{space 4}-10.71014{col 82}{space 3}-1.495139
{txt}{space 14}_Icowcode_452 {c |}{col 29}{res}{space 2}-6.063268{col 41}{space 2} 2.350552{col 52}{space 1}   -2.58{col 61}{space 3}0.010{col 69}{space 4}-10.67077{col 82}{space 3}-1.455769
{txt}{space 14}_Icowcode_461 {c |}{col 29}{res}{space 2}-6.059362{col 41}{space 2} 2.350552{col 52}{space 1}   -2.58{col 61}{space 3}0.010{col 69}{space 4}-10.66686{col 82}{space 3}-1.451863
{txt}{space 14}_Icowcode_471 {c |}{col 29}{res}{space 2}-6.061348{col 41}{space 2} 2.350552{col 52}{space 1}   -2.58{col 61}{space 3}0.010{col 69}{space 4}-10.66885{col 82}{space 3}-1.453849
{txt}{space 14}_Icowcode_475 {c |}{col 29}{res}{space 2}   -5.381{col 41}{space 2} 2.353334{col 52}{space 1}   -2.29{col 61}{space 3}0.022{col 69}{space 4}-9.993951{col 82}{space 3}-.7680485
{txt}{space 14}_Icowcode_481 {c |}{col 29}{res}{space 2}-5.696243{col 41}{space 2} 2.343672{col 52}{space 1}   -2.43{col 61}{space 3}0.015{col 69}{space 4}-10.29025{col 82}{space 3} -1.10223
{txt}{space 14}_Icowcode_482 {c |}{col 29}{res}{space 2}-5.732912{col 41}{space 2} 2.350678{col 52}{space 1}   -2.44{col 61}{space 3}0.015{col 69}{space 4}-10.34066{col 82}{space 3}-1.125167
{txt}{space 14}_Icowcode_483 {c |}{col 29}{res}{space 2} -5.83117{col 41}{space 2} 2.350409{col 52}{space 1}   -2.48{col 61}{space 3}0.013{col 69}{space 4}-10.43839{col 82}{space 3}-1.223951
{txt}{space 14}_Icowcode_484 {c |}{col 29}{res}{space 2}-5.866366{col 41}{space 2} 2.350334{col 52}{space 1}   -2.50{col 61}{space 3}0.013{col 69}{space 4}-10.47344{col 82}{space 3}-1.259294
{txt}{space 14}_Icowcode_490 {c |}{col 29}{res}{space 2}-5.376801{col 41}{space 2} 2.353334{col 52}{space 1}   -2.28{col 61}{space 3}0.022{col 69}{space 4}-9.989752{col 82}{space 3}-.7638492
{txt}{space 14}_Icowcode_500 {c |}{col 29}{res}{space 2}-5.380957{col 41}{space 2} 2.353334{col 52}{space 1}   -2.29{col 61}{space 3}0.022{col 69}{space 4}-9.993908{col 82}{space 3} -.768005
{txt}{space 14}_Icowcode_501 {c |}{col 29}{res}{space 2}-6.095429{col 41}{space 2} 2.350552{col 52}{space 1}   -2.59{col 61}{space 3}0.010{col 69}{space 4}-10.70293{col 82}{space 3} -1.48793
{txt}{space 14}_Icowcode_510 {c |}{col 29}{res}{space 2}-6.099198{col 41}{space 2} 2.350552{col 52}{space 1}   -2.59{col 61}{space 3}0.009{col 69}{space 4} -10.7067{col 82}{space 3}-1.491699
{txt}{space 14}_Icowcode_516 {c |}{col 29}{res}{space 2}-5.698955{col 41}{space 2} 2.350872{col 52}{space 1}   -2.42{col 61}{space 3}0.015{col 69}{space 4}-10.30708{col 82}{space 3} -1.09083
{txt}{space 14}_Icowcode_517 {c |}{col 29}{res}{space 2}-5.782933{col 41}{space 2} 2.350409{col 52}{space 1}   -2.46{col 61}{space 3}0.014{col 69}{space 4}-10.39015{col 82}{space 3}-1.175714
{txt}{space 14}_Icowcode_520 {c |}{col 29}{res}{space 2}-6.101024{col 41}{space 2} 2.350552{col 52}{space 1}   -2.60{col 61}{space 3}0.009{col 69}{space 4}-10.70852{col 82}{space 3}-1.493525
{txt}{space 14}_Icowcode_522 {c |}{col 29}{res}{space 2}-5.996586{col 41}{space 2} 2.350552{col 52}{space 1}   -2.55{col 61}{space 3}0.011{col 69}{space 4}-10.60408{col 82}{space 3}-1.389087
{txt}{space 14}_Icowcode_525 {c |}{col 29}{res}{space 2}-6.102638{col 41}{space 2} 2.350552{col 52}{space 1}   -2.60{col 61}{space 3}0.009{col 69}{space 4}-10.71014{col 82}{space 3}-1.495139
{txt}{space 14}_Icowcode_530 {c |}{col 29}{res}{space 2}-4.979458{col 41}{space 2} 2.359334{col 52}{space 1}   -2.11{col 61}{space 3}0.035{col 69}{space 4}-9.604172{col 82}{space 3}-.3547449
{txt}{space 14}_Icowcode_531 {c |}{col 29}{res}{space 2}        0{col 41}{txt}  (omitted)
{space 14}_Icowcode_540 {c |}{col 29}{res}{space 2}-5.091043{col 41}{space 2} 2.333502{col 52}{space 1}   -2.18{col 61}{space 3}0.029{col 69}{space 4}-9.665121{col 82}{space 3}-.5169653
{txt}{space 14}_Icowcode_541 {c |}{col 29}{res}{space 2}-5.203596{col 41}{space 2} 2.355608{col 52}{space 1}   -2.21{col 61}{space 3}0.027{col 69}{space 4}-9.821005{col 82}{space 3}-.5861868
{txt}{space 14}_Icowcode_551 {c |}{col 29}{res}{space 2}-5.331615{col 41}{space 2} 2.353843{col 52}{space 1}   -2.27{col 61}{space 3}0.024{col 69}{space 4}-9.945565{col 82}{space 3}-.7176646
{txt}{space 14}_Icowcode_552 {c |}{col 29}{res}{space 2}-4.170077{col 41}{space 2} 2.380798{col 52}{space 1}   -1.75{col 61}{space 3}0.080{col 69}{space 4}-8.836862{col 82}{space 3} .4967081
{txt}{space 14}_Icowcode_553 {c |}{col 29}{res}{space 2}-5.429834{col 41}{space 2} 2.352864{col 52}{space 1}   -2.31{col 61}{space 3}0.021{col 69}{space 4}-10.04186{col 82}{space 3}-.8178039
{txt}{space 14}_Icowcode_560 {c |}{col 29}{res}{space 2}-5.923448{col 41}{space 2} 2.350552{col 52}{space 1}   -2.52{col 61}{space 3}0.012{col 69}{space 4}-10.53095{col 82}{space 3}-1.315949
{txt}{space 14}_Icowcode_565 {c |}{col 29}{res}{space 2}-6.063078{col 41}{space 2} 2.350552{col 52}{space 1}   -2.58{col 61}{space 3}0.010{col 69}{space 4}-10.67058{col 82}{space 3}-1.455579
{txt}{space 14}_Icowcode_570 {c |}{col 29}{res}{space 2}-6.102638{col 41}{space 2} 2.350552{col 52}{space 1}   -2.60{col 61}{space 3}0.009{col 69}{space 4}-10.71014{col 82}{space 3}-1.495139
{txt}{space 14}_Icowcode_571 {c |}{col 29}{res}{space 2}-6.102638{col 41}{space 2} 2.350552{col 52}{space 1}   -2.60{col 61}{space 3}0.009{col 69}{space 4}-10.71014{col 82}{space 3}-1.495139
{txt}{space 14}_Icowcode_580 {c |}{col 29}{res}{space 2}-5.011058{col 41}{space 2} 2.332549{col 52}{space 1}   -2.15{col 61}{space 3}0.032{col 69}{space 4}-9.583269{col 82}{space 3}-.4388483
{txt}{space 14}_Icowcode_581 {c |}{col 29}{res}{space 2}-3.722055{col 41}{space 2} 2.321518{col 52}{space 1}   -1.60{col 61}{space 3}0.109{col 69}{space 4}-8.272642{col 82}{space 3} .8285315
{txt}{space 14}_Icowcode_590 {c |}{col 29}{res}{space 2}-2.488573{col 41}{space 2} 2.320362{col 52}{space 1}   -1.07{col 61}{space 3}0.284{col 69}{space 4}-7.036893{col 82}{space 3} 2.059746
{txt}{space 14}_Icowcode_591 {c |}{col 29}{res}{space 2} -1.99094{col 41}{space 2}  2.32038{col 52}{space 1}   -0.86{col 61}{space 3}0.391{col 69}{space 4}-6.539295{col 82}{space 3} 2.557415
{txt}{space 14}_Icowcode_600 {c |}{col 29}{res}{space 2}-5.337831{col 41}{space 2} 2.352864{col 52}{space 1}   -2.27{col 61}{space 3}0.023{col 69}{space 4}-9.949862{col 82}{space 3}-.7258014
{txt}{space 14}_Icowcode_615 {c |}{col 29}{res}{space 2}-5.993007{col 41}{space 2} 2.350552{col 52}{space 1}   -2.55{col 61}{space 3}0.011{col 69}{space 4}-10.60051{col 82}{space 3}-1.385508
{txt}{space 14}_Icowcode_616 {c |}{col 29}{res}{space 2} -5.09952{col 41}{space 2} 2.354392{col 52}{space 1}   -2.17{col 61}{space 3}0.030{col 69}{space 4}-9.714546{col 82}{space 3}-.4844943
{txt}{space 14}_Icowcode_620 {c |}{col 29}{res}{space 2}-6.101187{col 41}{space 2} 2.350552{col 52}{space 1}   -2.60{col 61}{space 3}0.009{col 69}{space 4}-10.70869{col 82}{space 3}-1.493688
{txt}{space 14}_Icowcode_625 {c |}{col 29}{res}{space 2}-5.957317{col 41}{space 2} 2.347695{col 52}{space 1}   -2.54{col 61}{space 3}0.011{col 69}{space 4}-10.55922{col 82}{space 3}-1.355418
{txt}{space 14}_Icowcode_630 {c |}{col 29}{res}{space 2}  -.80825{col 41}{space 2} 2.350552{col 52}{space 1}   -0.34{col 61}{space 3}0.731{col 69}{space 4}-5.415749{col 82}{space 3} 3.799249
{txt}{space 14}_Icowcode_640 {c |}{col 29}{res}{space 2}-4.662833{col 41}{space 2} 2.334491{col 52}{space 1}   -2.00{col 61}{space 3}0.046{col 69}{space 4}-9.238848{col 82}{space 3}-.0868176
{txt}{space 14}_Icowcode_645 {c |}{col 29}{res}{space 2}  -3.0054{col 41}{space 2} 2.320362{col 52}{space 1}   -1.30{col 61}{space 3}0.195{col 69}{space 4} -7.55372{col 82}{space 3}  1.54292
{txt}{space 14}_Icowcode_651 {c |}{col 29}{res}{space 2}-6.031885{col 41}{space 2} 2.350552{col 52}{space 1}   -2.57{col 61}{space 3}0.010{col 69}{space 4}-10.63938{col 82}{space 3}-1.424387
{txt}{space 14}_Icowcode_652 {c |}{col 29}{res}{space 2} -6.10006{col 41}{space 2} 2.350552{col 52}{space 1}   -2.60{col 61}{space 3}0.009{col 69}{space 4}-10.70756{col 82}{space 3}-1.492561
{txt}{space 14}_Icowcode_660 {c |}{col 29}{res}{space 2}-4.209058{col 41}{space 2} 2.333752{col 52}{space 1}   -1.80{col 61}{space 3}0.071{col 69}{space 4}-8.783625{col 82}{space 3}  .365509
{txt}{space 14}_Icowcode_663 {c |}{col 29}{res}{space 2}-4.393069{col 41}{space 2}  2.32832{col 52}{space 1}   -1.89{col 61}{space 3}0.059{col 69}{space 4}-8.956989{col 82}{space 3} .1708498
{txt}{space 14}_Icowcode_666 {c |}{col 29}{res}{space 2} .5076787{col 41}{space 2} 2.320362{col 52}{space 1}    0.22{col 61}{space 3}0.827{col 69}{space 4}-4.040641{col 82}{space 3} 5.055998
{txt}{space 14}_Icowcode_670 {c |}{col 29}{res}{space 2}-4.571352{col 41}{space 2} 2.331632{col 52}{space 1}   -1.96{col 61}{space 3}0.050{col 69}{space 4}-9.141764{col 82}{space 3}-.0009402
{txt}{space 14}_Icowcode_679 {c |}{col 29}{res}{space 2}-6.102638{col 41}{space 2} 2.350552{col 52}{space 1}   -2.60{col 61}{space 3}0.009{col 69}{space 4}-10.71014{col 82}{space 3}-1.495139
{txt}{space 14}_Icowcode_690 {c |}{col 29}{res}{space 2}-2.630554{col 41}{space 2} 2.320651{col 52}{space 1}   -1.13{col 61}{space 3}0.257{col 69}{space 4}-7.179441{col 82}{space 3} 1.918332
{txt}{space 14}_Icowcode_692 {c |}{col 29}{res}{space 2} .3057306{col 41}{space 2} 2.322548{col 52}{space 1}    0.13{col 61}{space 3}0.895{col 69}{space 4}-4.246874{col 82}{space 3} 4.858336
{txt}{space 14}_Icowcode_694 {c |}{col 29}{res}{space 2}-1.757441{col 41}{space 2} 2.347836{col 52}{space 1}   -0.75{col 61}{space 3}0.454{col 69}{space 4}-6.359616{col 82}{space 3} 2.844735
{txt}{space 14}_Icowcode_696 {c |}{col 29}{res}{space 2}-3.939884{col 41}{space 2} 2.373582{col 52}{space 1}   -1.66{col 61}{space 3}0.097{col 69}{space 4}-8.592526{col 82}{space 3} .7127585
{txt}{space 14}_Icowcode_698 {c |}{col 29}{res}{space 2}-4.429069{col 41}{space 2} 2.348582{col 52}{space 1}   -1.89{col 61}{space 3}0.059{col 69}{space 4}-9.032705{col 82}{space 3} .1745671
{txt}{space 14}_Icowcode_700 {c |}{col 29}{res}{space 2}-2.902799{col 41}{space 2} 2.320524{col 52}{space 1}   -1.25{col 61}{space 3}0.211{col 69}{space 4}-7.451437{col 82}{space 3}  1.64584
{txt}{space 14}_Icowcode_701 {c |}{col 29}{res}{space 2}-6.102638{col 41}{space 2} 2.350552{col 52}{space 1}   -2.60{col 61}{space 3}0.009{col 69}{space 4}-10.71014{col 82}{space 3}-1.495139
{txt}{space 14}_Icowcode_702 {c |}{col 29}{res}{space 2}-4.501746{col 41}{space 2} 2.341128{col 52}{space 1}   -1.92{col 61}{space 3}0.055{col 69}{space 4}-9.090771{col 82}{space 3} .0872795
{txt}{space 14}_Icowcode_703 {c |}{col 29}{res}{space 2}-6.013794{col 41}{space 2} 2.350552{col 52}{space 1}   -2.56{col 61}{space 3}0.011{col 69}{space 4}-10.62129{col 82}{space 3}-1.406295
{txt}{space 14}_Icowcode_704 {c |}{col 29}{res}{space 2}-5.489785{col 41}{space 2} 2.352042{col 52}{space 1}   -2.33{col 61}{space 3}0.020{col 69}{space 4} -10.1002{col 82}{space 3}-.8793658
{txt}{space 14}_Icowcode_705 {c |}{col 29}{res}{space 2}-5.447345{col 41}{space 2} 2.339964{col 52}{space 1}   -2.33{col 61}{space 3}0.020{col 69}{space 4}-10.03409{col 82}{space 3}-.8606002
{txt}{space 14}_Icowcode_710 {c |}{col 29}{res}{space 2}-5.138882{col 41}{space 2} 2.350552{col 52}{space 1}   -2.19{col 61}{space 3}0.029{col 69}{space 4}-9.746381{col 82}{space 3}-.5313827
{txt}{space 14}_Icowcode_712 {c |}{col 29}{res}{space 2}-3.017814{col 41}{space 2}  2.32038{col 52}{space 1}   -1.30{col 61}{space 3}0.193{col 69}{space 4}-7.566169{col 82}{space 3} 1.530541
{txt}{space 14}_Icowcode_713 {c |}{col 29}{res}{space 2}        0{col 41}{txt}  (omitted)
{space 14}_Icowcode_715 {c |}{col 29}{res}{space 2}-5.231409{col 41}{space 2} 2.350552{col 52}{space 1}   -2.23{col 61}{space 3}0.026{col 69}{space 4}-9.838908{col 82}{space 3}-.6239099
{txt}{space 14}_Icowcode_716 {c |}{col 29}{res}{space 2}-5.489295{col 41}{space 2} 2.350552{col 52}{space 1}   -2.34{col 61}{space 3}0.020{col 69}{space 4}-10.09679{col 82}{space 3}-.8817962
{txt}{space 14}_Icowcode_731 {c |}{col 29}{res}{space 2}-6.102638{col 41}{space 2} 2.350552{col 52}{space 1}   -2.60{col 61}{space 3}0.009{col 69}{space 4}-10.71014{col 82}{space 3}-1.495139
{txt}{space 14}_Icowcode_732 {c |}{col 29}{res}{space 2}-2.282701{col 41}{space 2} 2.542798{col 52}{space 1}   -0.90{col 61}{space 3}0.369{col 69}{space 4}-7.267036{col 82}{space 3} 2.701634
{txt}{space 14}_Icowcode_740 {c |}{col 29}{res}{space 2}-4.442273{col 41}{space 2} 2.564989{col 52}{space 1}   -1.73{col 61}{space 3}0.083{col 69}{space 4}-9.470106{col 82}{space 3} .5855593
{txt}{space 14}_Icowcode_750 {c |}{col 29}{res}{space 2}-5.332909{col 41}{space 2} 2.353843{col 52}{space 1}   -2.27{col 61}{space 3}0.023{col 69}{space 4}-9.946859{col 82}{space 3} -.718959
{txt}{space 14}_Icowcode_760 {c |}{col 29}{res}{space 2}-6.054493{col 41}{space 2} 2.350552{col 52}{space 1}   -2.58{col 61}{space 3}0.010{col 69}{space 4}-10.66199{col 82}{space 3}-1.446995
{txt}{space 14}_Icowcode_770 {c |}{col 29}{res}{space 2}-6.014155{col 41}{space 2} 2.350552{col 52}{space 1}   -2.56{col 61}{space 3}0.011{col 69}{space 4}-10.62165{col 82}{space 3}-1.406657
{txt}{space 14}_Icowcode_771 {c |}{col 29}{res}{space 2} -5.96425{col 41}{space 2} 2.350303{col 52}{space 1}   -2.54{col 61}{space 3}0.011{col 69}{space 4}-10.57126{col 82}{space 3} -1.35724
{txt}{space 14}_Icowcode_775 {c |}{col 29}{res}{space 2}-2.993292{col 41}{space 2} 2.326876{col 52}{space 1}   -1.29{col 61}{space 3}0.198{col 69}{space 4}-7.554381{col 82}{space 3} 1.567796
{txt}{space 14}_Icowcode_780 {c |}{col 29}{res}{space 2}-6.027775{col 41}{space 2} 2.350552{col 52}{space 1}   -2.56{col 61}{space 3}0.010{col 69}{space 4}-10.63527{col 82}{space 3}-1.420276
{txt}{space 14}_Icowcode_781 {c |}{col 29}{res}{space 2}-4.434926{col 41}{space 2} 2.330751{col 52}{space 1}   -1.90{col 61}{space 3}0.057{col 69}{space 4} -9.00361{col 82}{space 3} .1337579
{txt}{space 14}_Icowcode_790 {c |}{col 29}{res}{space 2}-5.235937{col 41}{space 2} 2.335515{col 52}{space 1}   -2.24{col 61}{space 3}0.025{col 69}{space 4}-9.813959{col 82}{space 3}-.6579143
{txt}{space 14}_Icowcode_800 {c |}{col 29}{res}{space 2} -5.00936{col 41}{space 2} 2.355608{col 52}{space 1}   -2.13{col 61}{space 3}0.033{col 69}{space 4}-9.626769{col 82}{space 3}-.3919512
{txt}{space 14}_Icowcode_811 {c |}{col 29}{res}{space 2}-6.012638{col 41}{space 2} 2.350552{col 52}{space 1}   -2.56{col 61}{space 3}0.011{col 69}{space 4}-10.62014{col 82}{space 3}-1.405139
{txt}{space 14}_Icowcode_812 {c |}{col 29}{res}{space 2}-6.096808{col 41}{space 2} 2.350552{col 52}{space 1}   -2.59{col 61}{space 3}0.010{col 69}{space 4}-10.70431{col 82}{space 3} -1.48931
{txt}{space 14}_Icowcode_817 {c |}{col 29}{res}{space 2} -4.48656{col 41}{space 2} 2.335639{col 52}{space 1}   -1.92{col 61}{space 3}0.055{col 69}{space 4}-9.064827{col 82}{space 3}  .091707
{txt}{space 14}_Icowcode_820 {c |}{col 29}{res}{space 2} -1.59867{col 41}{space 2} 2.598284{col 52}{space 1}   -0.62{col 61}{space 3}0.538{col 69}{space 4}-6.691768{col 82}{space 3} 3.494427
{txt}{space 14}_Icowcode_830 {c |}{col 29}{res}{space 2}-1.148623{col 41}{space 2} 2.320651{col 52}{space 1}   -0.49{col 61}{space 3}0.621{col 69}{space 4}-5.697509{col 82}{space 3} 3.400264
{txt}{space 14}_Icowcode_835 {c |}{col 29}{res}{space 2}-.7958144{col 41}{space 2} 2.581288{col 52}{space 1}   -0.31{col 61}{space 3}0.758{col 69}{space 4}-5.855595{col 82}{space 3} 4.263967
{txt}{space 14}_Icowcode_840 {c |}{col 29}{res}{space 2}-2.624773{col 41}{space 2} 2.321012{col 52}{space 1}   -1.13{col 61}{space 3}0.258{col 69}{space 4}-7.174368{col 82}{space 3} 1.924822
{txt}{space 14}_Icowcode_850 {c |}{col 29}{res}{space 2}-3.100094{col 41}{space 2} 2.320362{col 52}{space 1}   -1.34{col 61}{space 3}0.182{col 69}{space 4}-7.648414{col 82}{space 3} 1.448226
{txt}{space 14}_Icowcode_860 {c |}{col 29}{res}{space 2}-1.438768{col 41}{space 2} 2.353583{col 52}{space 1}   -0.61{col 61}{space 3}0.541{col 69}{space 4}-6.052208{col 82}{space 3} 3.174672
{txt}{space 14}_Icowcode_900 {c |}{col 29}{res}{space 2}-1.653033{col 41}{space 2}  2.32038{col 52}{space 1}   -0.71{col 61}{space 3}0.476{col 69}{space 4}-6.201388{col 82}{space 3} 2.895322
{txt}{space 14}_Icowcode_910 {c |}{col 29}{res}{space 2} -3.10967{col 41}{space 2} 2.626538{col 52}{space 1}   -1.18{col 61}{space 3}0.236{col 69}{space 4} -8.25815{col 82}{space 3}  2.03881
{txt}{space 14}_Icowcode_920 {c |}{col 29}{res}{space 2}-2.511233{col 41}{space 2} 2.320362{col 52}{space 1}   -1.08{col 61}{space 3}0.279{col 69}{space 4}-7.059553{col 82}{space 3} 2.037087
{txt}{space 14}_Icowcode_935 {c |}{col 29}{res}{space 2}-3.649916{col 41}{space 2} 2.321247{col 52}{space 1}   -1.57{col 61}{space 3}0.116{col 69}{space 4}-8.199972{col 82}{space 3} .9001391
{txt}{space 14}_Icowcode_940 {c |}{col 29}{res}{space 2}-4.804138{col 41}{space 2} 2.329905{col 52}{space 1}   -2.06{col 61}{space 3}0.039{col 69}{space 4}-9.371164{col 82}{space 3}-.2371125
{txt}{space 14}_Icowcode_946 {c |}{col 29}{res}{space 2}-6.102638{col 41}{space 2} 2.350552{col 52}{space 1}   -2.60{col 61}{space 3}0.009{col 69}{space 4}-10.71014{col 82}{space 3}-1.495139
{txt}{space 14}_Icowcode_947 {c |}{col 29}{res}{space 2}-3.217083{col 41}{space 2}  2.32038{col 52}{space 1}   -1.39{col 61}{space 3}0.166{col 69}{space 4}-7.765438{col 82}{space 3} 1.331272
{txt}{space 14}_Icowcode_950 {c |}{col 29}{res}{space 2}-3.076429{col 41}{space 2} 2.320362{col 52}{space 1}   -1.33{col 61}{space 3}0.185{col 69}{space 4}-7.624748{col 82}{space 3} 1.471891
{txt}{space 14}_Icowcode_955 {c |}{col 29}{res}{space 2}-3.217083{col 41}{space 2}  2.32038{col 52}{space 1}   -1.39{col 61}{space 3}0.166{col 69}{space 4}-7.765438{col 82}{space 3} 1.331272
{txt}{space 14}_Icowcode_970 {c |}{col 29}{res}{space 2}-4.731999{col 41}{space 2} 2.329094{col 52}{space 1}   -2.03{col 61}{space 3}0.042{col 69}{space 4}-9.297437{col 82}{space 3}-.1665619
{txt}{space 14}_Icowcode_983 {c |}{col 29}{res}{space 2}-2.495695{col 41}{space 2} 2.321825{col 52}{space 1}   -1.07{col 61}{space 3}0.282{col 69}{space 4}-7.046884{col 82}{space 3} 2.055494
{txt}{space 14}_Icowcode_986 {c |}{col 29}{res}{space 2}-3.072806{col 41}{space 2}  2.32038{col 52}{space 1}   -1.32{col 61}{space 3}0.185{col 69}{space 4}-7.621161{col 82}{space 3} 1.475549
{txt}{space 14}_Icowcode_987 {c |}{col 29}{res}{space 2} -5.25042{col 41}{space 2} 2.354981{col 52}{space 1}   -2.23{col 61}{space 3}0.026{col 69}{space 4}-9.866599{col 82}{space 3}-.6342406
{txt}{space 14}_Icowcode_990 {c |}{col 29}{res}{space 2}-4.371305{col 41}{space 2}  2.32558{col 52}{space 1}   -1.88{col 61}{space 3}0.060{col 69}{space 4}-8.929854{col 82}{space 3} .1872435
{txt}{space 13}_Icowcode_1013 {c |}{col 29}{res}{space 2}-2.856389{col 41}{space 2} 2.320651{col 52}{space 1}   -1.23{col 61}{space 3}0.218{col 69}{space 4}-7.405276{col 82}{space 3} 1.692498
{txt}{space 13}_Icowcode_1015 {c |}{col 29}{res}{space 2}-4.546053{col 41}{space 2} 2.355209{col 52}{space 1}   -1.93{col 61}{space 3}0.054{col 69}{space 4} -9.16268{col 82}{space 3} .0705738
{txt}{space 13}_Icowcode_1020 {c |}{col 29}{res}{space 2}-4.636748{col 41}{space 2} 2.330751{col 52}{space 1}   -1.99{col 61}{space 3}0.047{col 69}{space 4}-9.205432{col 82}{space 3}-.0680636
{txt}{space 13}_Icowcode_1027 {c |}{col 29}{res}{space 2}        0{col 41}{txt}  (omitted)
{space 22}_cons {c |}{col 29}{res}{space 2}-4.355558{col 41}{space 2}  2.65135{col 52}{space 1}   -1.64{col 61}{space 3}0.100{col 69}{space 4}-9.552674{col 82}{space 3} .8415573
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est2{txt} stored)

{com}. 
. eststo: xi: regress deaths_7_day_pc L7be_cat_1_China_carryover L7be_cat_2_China_carryover L7be_cat_3_China_carryover  time time_sq time_cube i.cowcode
{txt}i.cowcode{col 19}_Icowcode_2-1027{col 39}(naturally coded; _Icowcode_2 omitted)
note: _Icowcode_531 omitted because of collinearity
note: _Icowcode_713 omitted because of collinearity
note: _Icowcode_1027 omitted because of collinearity

      Source {c |}       SS           df       MS      Number of obs   ={res}    12,672
{txt}{hline 13}{c +}{hline 34}   F(197, 12474)   = {res}    19.00
{txt}       Model {c |} {res} 6185.28841       197  31.3974031   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res}  20609.434    12,474  1.65219127   {txt}R-squared       ={res}    0.2308
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.2187
{txt}       Total {c |} {res} 26794.7224    12,671  2.11464939   {txt}Root MSE        =   {res} 1.2854

{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           deaths_7_day_pc{col 28}{c |}      Coef.{col 40}   Std. Err.{col 52}      t{col 60}   P>|t|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
L7be_cat_1_China_carryover {c |}{col 28}{res}{space 2}-.2462025{col 40}{space 2} .0495519{col 51}{space 1}   -4.97{col 60}{space 3}0.000{col 68}{space 4}-.3433318{col 81}{space 3}-.1490732
{txt}L7be_cat_2_China_carryover {c |}{col 28}{res}{space 2}-.1519601{col 40}{space 2} .1271767{col 51}{space 1}   -1.19{col 60}{space 3}0.232{col 68}{space 4}-.4012461{col 81}{space 3}  .097326
{txt}L7be_cat_3_China_carryover {c |}{col 28}{res}{space 2}-.2731997{col 40}{space 2}  .050955{col 51}{space 1}   -5.36{col 60}{space 3}0.000{col 68}{space 4}-.3730793{col 81}{space 3}  -.17332
{txt}{space 22}time {c |}{col 28}{res}{space 2}  .020683{col 40}{space 2} .0094782{col 51}{space 1}    2.18{col 60}{space 3}0.029{col 68}{space 4} .0021042{col 81}{space 3} .0392617
{txt}{space 19}time_sq {c |}{col 28}{res}{space 2}-.0007165{col 40}{space 2} .0002588{col 51}{space 1}   -2.77{col 60}{space 3}0.006{col 68}{space 4}-.0012237{col 81}{space 3}-.0002093
{txt}{space 17}time_cube {c |}{col 28}{res}{space 2} 8.29e-06{col 40}{space 2} 2.11e-06{col 51}{space 1}    3.93{col 60}{space 3}0.000{col 68}{space 4} 4.15e-06{col 81}{space 3} .0000124
{txt}{space 14}_Icowcode_20 {c |}{col 28}{res}{space 2}-.2204947{col 40}{space 2} .2266387{col 51}{space 1}   -0.97{col 60}{space 3}0.331{col 68}{space 4}-.6647416{col 81}{space 3} .2237522
{txt}{space 14}_Icowcode_31 {c |}{col 28}{res}{space 2}-.0426884{col 40}{space 2} .2237666{col 51}{space 1}   -0.19{col 60}{space 3}0.849{col 68}{space 4}-.4813054{col 81}{space 3} .3959286
{txt}{space 14}_Icowcode_40 {c |}{col 28}{res}{space 2}-.2312177{col 40}{space 2} .2266387{col 51}{space 1}   -1.02{col 60}{space 3}0.308{col 68}{space 4}-.6754645{col 81}{space 3} .2130292
{txt}{space 14}_Icowcode_41 {c |}{col 28}{res}{space 2}-.1881717{col 40}{space 2} .2253817{col 51}{space 1}   -0.83{col 60}{space 3}0.404{col 68}{space 4}-.6299547{col 81}{space 3} .2536113
{txt}{space 14}_Icowcode_42 {c |}{col 28}{res}{space 2}-.2219373{col 40}{space 2} .2266387{col 51}{space 1}   -0.98{col 60}{space 3}0.327{col 68}{space 4}-.6661842{col 81}{space 3} .2223096
{txt}{space 14}_Icowcode_51 {c |}{col 28}{res}{space 2}-.0412561{col 40}{space 2} .2237603{col 51}{space 1}   -0.18{col 60}{space 3}0.854{col 68}{space 4}-.4798607{col 81}{space 3} .3973485
{txt}{space 14}_Icowcode_52 {c |}{col 28}{res}{space 2}-.1409085{col 40}{space 2} .2245411{col 51}{space 1}   -0.63{col 60}{space 3}0.530{col 68}{space 4}-.5810437{col 81}{space 3} .2992266
{txt}{space 14}_Icowcode_53 {c |}{col 28}{res}{space 2}-.1956813{col 40}{space 2} .2266443{col 51}{space 1}   -0.86{col 60}{space 3}0.388{col 68}{space 4} -.639939{col 81}{space 3} .2485764
{txt}{space 14}_Icowcode_54 {c |}{col 28}{res}{space 2}-.0052694{col 40}{space 2} .2299749{col 51}{space 1}   -0.02{col 60}{space 3}0.982{col 68}{space 4}-.4560557{col 81}{space 3} .4455169
{txt}{space 14}_Icowcode_55 {c |}{col 28}{res}{space 2}  .004072{col 40}{space 2} .2240161{col 51}{space 1}    0.02{col 60}{space 3}0.985{col 68}{space 4}-.4350341{col 81}{space 3} .4431782
{txt}{space 14}_Icowcode_56 {c |}{col 28}{res}{space 2} .0297265{col 40}{space 2} .2243936{col 51}{space 1}    0.13{col 60}{space 3}0.895{col 68}{space 4}-.4101196{col 81}{space 3} .4695725
{txt}{space 14}_Icowcode_58 {c |}{col 28}{res}{space 2}-.0464187{col 40}{space 2} .2237603{col 51}{space 1}   -0.21{col 60}{space 3}0.836{col 68}{space 4}-.4850234{col 81}{space 3} .3921859
{txt}{space 14}_Icowcode_60 {c |}{col 28}{res}{space 2}-.0301058{col 40}{space 2} .2292314{col 51}{space 1}   -0.13{col 60}{space 3}0.896{col 68}{space 4}-.4794346{col 81}{space 3} .4192231
{txt}{space 14}_Icowcode_70 {c |}{col 28}{res}{space 2}-.1453225{col 40}{space 2} .2269828{col 51}{space 1}   -0.64{col 60}{space 3}0.522{col 68}{space 4}-.5902437{col 81}{space 3} .2995987
{txt}{space 14}_Icowcode_80 {c |}{col 28}{res}{space 2}-.0762615{col 40}{space 2} .2238006{col 51}{space 1}   -0.34{col 60}{space 3}0.733{col 68}{space 4}-.5149451{col 81}{space 3} .3624222
{txt}{space 14}_Icowcode_90 {c |}{col 28}{res}{space 2}-.0455403{col 40}{space 2} .2237603{col 51}{space 1}   -0.20{col 60}{space 3}0.839{col 68}{space 4}-.4841449{col 81}{space 3} .3930644
{txt}{space 14}_Icowcode_91 {c |}{col 28}{res}{space 2}-.0747295{col 40}{space 2}  .226575{col 51}{space 1}   -0.33{col 60}{space 3}0.742{col 68}{space 4}-.5188515{col 81}{space 3} .3693924
{txt}{space 14}_Icowcode_92 {c |}{col 28}{res}{space 2}-.0464187{col 40}{space 2} .2237603{col 51}{space 1}   -0.21{col 60}{space 3}0.836{col 68}{space 4}-.4850234{col 81}{space 3} .3921859
{txt}{space 14}_Icowcode_93 {c |}{col 28}{res}{space 2} -.232601{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.305{col 68}{space 4}-.6768479{col 81}{space 3} .2116458
{txt}{space 14}_Icowcode_94 {c |}{col 28}{res}{space 2}-.1399474{col 40}{space 2} .2269828{col 51}{space 1}   -0.62{col 60}{space 3}0.538{col 68}{space 4}-.5848686{col 81}{space 3} .3049739
{txt}{space 14}_Icowcode_95 {c |}{col 28}{res}{space 2}-.0129985{col 40}{space 2} .2291164{col 51}{space 1}   -0.06{col 60}{space 3}0.955{col 68}{space 4}-.4621019{col 81}{space 3} .4361049
{txt}{space 13}_Icowcode_100 {c |}{col 28}{res}{space 2}-.2318895{col 40}{space 2} .2266387{col 51}{space 1}   -1.02{col 60}{space 3}0.306{col 68}{space 4}-.6761364{col 81}{space 3} .2123574
{txt}{space 13}_Icowcode_101 {c |}{col 28}{res}{space 2}-.1376549{col 40}{space 2} .2270759{col 51}{space 1}   -0.61{col 60}{space 3}0.544{col 68}{space 4}-.5827586{col 81}{space 3} .3074488
{txt}{space 13}_Icowcode_110 {c |}{col 28}{res}{space 2}-.1762315{col 40}{space 2} .2266443{col 51}{space 1}   -0.78{col 60}{space 3}0.437{col 68}{space 4}-.6204892{col 81}{space 3} .2680263
{txt}{space 13}_Icowcode_115 {c |}{col 28}{res}{space 2}-.0650704{col 40}{space 2} .2237666{col 51}{space 1}   -0.29{col 60}{space 3}0.771{col 68}{space 4}-.5036874{col 81}{space 3} .3735465
{txt}{space 13}_Icowcode_130 {c |}{col 28}{res}{space 2}-.1210913{col 40}{space 2}  .227028{col 51}{space 1}   -0.53{col 60}{space 3}0.594{col 68}{space 4}-.5661012{col 81}{space 3} .3239186
{txt}{space 13}_Icowcode_135 {c |}{col 28}{res}{space 2} -.229485{col 40}{space 2} .2266387{col 51}{space 1}   -1.01{col 60}{space 3}0.311{col 68}{space 4}-.6737319{col 81}{space 3} .2147619
{txt}{space 13}_Icowcode_140 {c |}{col 28}{res}{space 2}-.2293398{col 40}{space 2} .2266387{col 51}{space 1}   -1.01{col 60}{space 3}0.312{col 68}{space 4}-.6735867{col 81}{space 3}  .214907
{txt}{space 13}_Icowcode_145 {c |}{col 28}{res}{space 2}-.2068234{col 40}{space 2} .2258627{col 51}{space 1}   -0.92{col 60}{space 3}0.360{col 68}{space 4}-.6495491{col 81}{space 3} .2359022
{txt}{space 13}_Icowcode_150 {c |}{col 28}{res}{space 2}-.1333735{col 40}{space 2} .2270759{col 51}{space 1}   -0.59{col 60}{space 3}0.557{col 68}{space 4}-.5784773{col 81}{space 3} .3117302
{txt}{space 13}_Icowcode_155 {c |}{col 28}{res}{space 2}-.2019954{col 40}{space 2} .2266246{col 51}{space 1}   -0.89{col 60}{space 3}0.373{col 68}{space 4}-.6462146{col 81}{space 3} .2422238
{txt}{space 13}_Icowcode_160 {c |}{col 28}{res}{space 2}-.1892069{col 40}{space 2}  .226658{col 51}{space 1}   -0.83{col 60}{space 3}0.404{col 68}{space 4}-.6334916{col 81}{space 3} .2550778
{txt}{space 13}_Icowcode_165 {c |}{col 28}{res}{space 2}-.1998207{col 40}{space 2} .2266331{col 51}{space 1}   -0.88{col 60}{space 3}0.378{col 68}{space 4}-.6440566{col 81}{space 3} .2444152
{txt}{space 13}_Icowcode_200 {c |}{col 28}{res}{space 2} .0854632{col 40}{space 2} .2286817{col 51}{space 1}    0.37{col 60}{space 3}0.709{col 68}{space 4}-.3627881{col 81}{space 3} .5337145
{txt}{space 13}_Icowcode_205 {c |}{col 28}{res}{space 2}-.2018067{col 40}{space 2} .2266387{col 51}{space 1}   -0.89{col 60}{space 3}0.373{col 68}{space 4}-.6460535{col 81}{space 3} .2424402
{txt}{space 13}_Icowcode_210 {c |}{col 28}{res}{space 2} .0367536{col 40}{space 2} .2266387{col 51}{space 1}    0.16{col 60}{space 3}0.871{col 68}{space 4}-.4074933{col 81}{space 3} .4810005
{txt}{space 13}_Icowcode_211 {c |}{col 28}{res}{space 2}-.0362361{col 40}{space 2} .2266387{col 51}{space 1}   -0.16{col 60}{space 3}0.873{col 68}{space 4} -.480483{col 81}{space 3} .4080107
{txt}{space 13}_Icowcode_212 {c |}{col 28}{res}{space 2}-.0050747{col 40}{space 2} .2266387{col 51}{space 1}   -0.02{col 60}{space 3}0.982{col 68}{space 4}-.4493216{col 81}{space 3} .4391721
{txt}{space 13}_Icowcode_220 {c |}{col 28}{res}{space 2} .0330658{col 40}{space 2} .2266387{col 51}{space 1}    0.15{col 60}{space 3}0.884{col 68}{space 4}-.4111811{col 81}{space 3} .4773127
{txt}{space 13}_Icowcode_223 {c |}{col 28}{res}{space 2}-.2329358{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.304{col 68}{space 4}-.6771827{col 81}{space 3} .2113111
{txt}{space 13}_Icowcode_225 {c |}{col 28}{res}{space 2} .0187733{col 40}{space 2} .2266387{col 51}{space 1}    0.08{col 60}{space 3}0.934{col 68}{space 4}-.4254735{col 81}{space 3} .4630202
{txt}{space 13}_Icowcode_230 {c |}{col 28}{res}{space 2} .7576284{col 40}{space 2} .2266387{col 51}{space 1}    3.34{col 60}{space 3}0.001{col 68}{space 4} .3133816{col 81}{space 3} 1.201875
{txt}{space 13}_Icowcode_235 {c |}{col 28}{res}{space 2}-.1780015{col 40}{space 2} .2266387{col 51}{space 1}   -0.79{col 60}{space 3}0.432{col 68}{space 4}-.6222483{col 81}{space 3} .2662454
{txt}{space 13}_Icowcode_255 {c |}{col 28}{res}{space 2}-.1997004{col 40}{space 2} .2266387{col 51}{space 1}   -0.88{col 60}{space 3}0.378{col 68}{space 4}-.6439472{col 81}{space 3} .2445465
{txt}{space 13}_Icowcode_290 {c |}{col 28}{res}{space 2}-.2286039{col 40}{space 2} .2266387{col 51}{space 1}   -1.01{col 60}{space 3}0.313{col 68}{space 4}-.6728508{col 81}{space 3}  .215643
{txt}{space 13}_Icowcode_305 {c |}{col 28}{res}{space 2} -.131847{col 40}{space 2} .2266934{col 51}{space 1}   -0.58{col 60}{space 3}0.561{col 68}{space 4}-.5762011{col 81}{space 3} .3125071
{txt}{space 13}_Icowcode_310 {c |}{col 28}{res}{space 2}-.1832329{col 40}{space 2} .2255667{col 51}{space 1}   -0.81{col 60}{space 3}0.417{col 68}{space 4}-.6253785{col 81}{space 3} .2589127
{txt}{space 13}_Icowcode_316 {c |}{col 28}{res}{space 2}-.2009183{col 40}{space 2} .2258627{col 51}{space 1}   -0.89{col 60}{space 3}0.374{col 68}{space 4} -.643644{col 81}{space 3} .2418073
{txt}{space 13}_Icowcode_317 {c |}{col 28}{res}{space 2}-.2325384{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.305{col 68}{space 4}-.6767853{col 81}{space 3} .2117085
{txt}{space 13}_Icowcode_325 {c |}{col 28}{res}{space 2}  1.48837{col 40}{space 2} .2266387{col 51}{space 1}    6.57{col 60}{space 3}0.000{col 68}{space 4} 1.044123{col 81}{space 3} 1.932617
{txt}{space 13}_Icowcode_331 {c |}{col 28}{res}{space 2} 8.992706{col 40}{space 2} .2266387{col 51}{space 1}   39.68{col 60}{space 3}0.000{col 68}{space 4} 8.548459{col 81}{space 3} 9.436953
{txt}{space 13}_Icowcode_338 {c |}{col 28}{res}{space 2}  -.16952{col 40}{space 2} .2249624{col 51}{space 1}   -0.75{col 60}{space 3}0.451{col 68}{space 4}-.6104811{col 81}{space 3} .2714411
{txt}{space 13}_Icowcode_339 {c |}{col 28}{res}{space 2}-.1177049{col 40}{space 2}  .227028{col 51}{space 1}   -0.52{col 60}{space 3}0.604{col 68}{space 4}-.5627148{col 81}{space 3}  .327305
{txt}{space 13}_Icowcode_341 {c |}{col 28}{res}{space 2}-.1902159{col 40}{space 2} .2276563{col 51}{space 1}   -0.84{col 60}{space 3}0.403{col 68}{space 4}-.6364573{col 81}{space 3} .2560254
{txt}{space 13}_Icowcode_343 {c |}{col 28}{res}{space 2}-.1231818{col 40}{space 2} .2270759{col 51}{space 1}   -0.54{col 60}{space 3}0.588{col 68}{space 4}-.5682855{col 81}{space 3} .3219219
{txt}{space 13}_Icowcode_344 {c |}{col 28}{res}{space 2} -.127766{col 40}{space 2} .2271264{col 51}{space 1}   -0.56{col 60}{space 3}0.574{col 68}{space 4}-.5729687{col 81}{space 3} .3174367
{txt}{space 13}_Icowcode_346 {c |}{col 28}{res}{space 2}-.0941709{col 40}{space 2} .2278544{col 51}{space 1}   -0.41{col 60}{space 3}0.679{col 68}{space 4}-.5408006{col 81}{space 3} .3524589
{txt}{space 13}_Icowcode_347 {c |}{col 28}{res}{space 2}-.2270709{col 40}{space 2} .2266387{col 51}{space 1}   -1.00{col 60}{space 3}0.316{col 68}{space 4}-.6713178{col 81}{space 3}  .217176
{txt}{space 13}_Icowcode_349 {c |}{col 28}{res}{space 2}-.2016251{col 40}{space 2} .2266387{col 51}{space 1}   -0.89{col 60}{space 3}0.374{col 68}{space 4} -.645872{col 81}{space 3} .2426218
{txt}{space 13}_Icowcode_350 {c |}{col 28}{res}{space 2}-.2044972{col 40}{space 2} .2266387{col 51}{space 1}   -0.90{col 60}{space 3}0.367{col 68}{space 4}-.6487441{col 81}{space 3} .2397496
{txt}{space 13}_Icowcode_352 {c |}{col 28}{res}{space 2}-.1127487{col 40}{space 2}  .227028{col 51}{space 1}   -0.50{col 60}{space 3}0.619{col 68}{space 4}-.5577586{col 81}{space 3} .3322612
{txt}{space 13}_Icowcode_355 {c |}{col 28}{res}{space 2}-.0132625{col 40}{space 2} .2284977{col 51}{space 1}   -0.06{col 60}{space 3}0.954{col 68}{space 4}-.4611531{col 81}{space 3} .4346282
{txt}{space 13}_Icowcode_359 {c |}{col 28}{res}{space 2} -.185503{col 40}{space 2}  .225473{col 51}{space 1}   -0.82{col 60}{space 3}0.411{col 68}{space 4}-.6274649{col 81}{space 3} .2564588
{txt}{space 13}_Icowcode_360 {c |}{col 28}{res}{space 2}-.1446136{col 40}{space 2} .2269001{col 51}{space 1}   -0.64{col 60}{space 3}0.524{col 68}{space 4}-.5893728{col 81}{space 3} .3001456
{txt}{space 13}_Icowcode_365 {c |}{col 28}{res}{space 2}-.0499393{col 40}{space 2} .2237565{col 51}{space 1}   -0.22{col 60}{space 3}0.823{col 68}{space 4}-.4885365{col 81}{space 3} .3886579
{txt}{space 13}_Icowcode_366 {c |}{col 28}{res}{space 2}-.2280238{col 40}{space 2} .2266387{col 51}{space 1}   -1.01{col 60}{space 3}0.314{col 68}{space 4}-.6722707{col 81}{space 3}  .216223
{txt}{space 13}_Icowcode_367 {c |}{col 28}{res}{space 2}-.2329358{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.304{col 68}{space 4}-.6771827{col 81}{space 3} .2113111
{txt}{space 13}_Icowcode_368 {c |}{col 28}{res}{space 2}-.2190288{col 40}{space 2} .2266387{col 51}{space 1}   -0.97{col 60}{space 3}0.334{col 68}{space 4}-.6632757{col 81}{space 3} .2252181
{txt}{space 13}_Icowcode_369 {c |}{col 28}{res}{space 2}-.2316261{col 40}{space 2} .2266387{col 51}{space 1}   -1.02{col 60}{space 3}0.307{col 68}{space 4} -.675873{col 81}{space 3} .2126208
{txt}{space 13}_Icowcode_370 {c |}{col 28}{res}{space 2}-.1294511{col 40}{space 2} .2271795{col 51}{space 1}   -0.57{col 60}{space 3}0.569{col 68}{space 4}-.5747578{col 81}{space 3} .3158557
{txt}{space 13}_Icowcode_371 {c |}{col 28}{res}{space 2}-.2128175{col 40}{space 2} .2260722{col 51}{space 1}   -0.94{col 60}{space 3}0.347{col 68}{space 4}-.6559539{col 81}{space 3} .2303189
{txt}{space 13}_Icowcode_372 {c |}{col 28}{res}{space 2}-.2329358{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.304{col 68}{space 4}-.6771827{col 81}{space 3} .2113111
{txt}{space 13}_Icowcode_373 {c |}{col 28}{res}{space 2}-.2096257{col 40}{space 2} .2266155{col 51}{space 1}   -0.93{col 60}{space 3}0.355{col 68}{space 4}-.6538271{col 81}{space 3} .2345756
{txt}{space 13}_Icowcode_375 {c |}{col 28}{res}{space 2}-.2254795{col 40}{space 2} .2266387{col 51}{space 1}   -0.99{col 60}{space 3}0.320{col 68}{space 4}-.6697264{col 81}{space 3} .2187674
{txt}{space 13}_Icowcode_380 {c |}{col 28}{res}{space 2}-.0640337{col 40}{space 2} .2266387{col 51}{space 1}   -0.28{col 60}{space 3}0.778{col 68}{space 4}-.5082806{col 81}{space 3} .3802131
{txt}{space 13}_Icowcode_385 {c |}{col 28}{res}{space 2}-.1991149{col 40}{space 2} .2266387{col 51}{space 1}   -0.88{col 60}{space 3}0.380{col 68}{space 4}-.6433618{col 81}{space 3}  .245132
{txt}{space 13}_Icowcode_390 {c |}{col 28}{res}{space 2} -.076205{col 40}{space 2} .2269001{col 51}{space 1}   -0.34{col 60}{space 3}0.737{col 68}{space 4}-.5209642{col 81}{space 3} .3685542
{txt}{space 13}_Icowcode_395 {c |}{col 28}{res}{space 2} -.066088{col 40}{space 2} .2271264{col 51}{space 1}   -0.29{col 60}{space 3}0.771{col 68}{space 4}-.5112907{col 81}{space 3} .3791146
{txt}{space 13}_Icowcode_403 {c |}{col 28}{res}{space 2}-.2329358{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.304{col 68}{space 4}-.6771827{col 81}{space 3} .2113111
{txt}{space 13}_Icowcode_404 {c |}{col 28}{res}{space 2}-.2329358{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.304{col 68}{space 4}-.6771827{col 81}{space 3} .2113111
{txt}{space 13}_Icowcode_411 {c |}{col 28}{res}{space 2}-.0771774{col 40}{space 2} .2247287{col 51}{space 1}   -0.34{col 60}{space 3}0.731{col 68}{space 4}-.5176803{col 81}{space 3} .3633255
{txt}{space 13}_Icowcode_420 {c |}{col 28}{res}{space 2}-.2281893{col 40}{space 2} .2266387{col 51}{space 1}   -1.01{col 60}{space 3}0.314{col 68}{space 4}-.6724362{col 81}{space 3} .2160576
{txt}{space 13}_Icowcode_432 {c |}{col 28}{res}{space 2}-.2329358{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.304{col 68}{space 4}-.6771827{col 81}{space 3} .2113111
{txt}{space 13}_Icowcode_433 {c |}{col 28}{res}{space 2}-.2329358{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.304{col 68}{space 4}-.6771827{col 81}{space 3} .2113111
{txt}{space 13}_Icowcode_434 {c |}{col 28}{res}{space 2}-.2329358{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.304{col 68}{space 4}-.6771827{col 81}{space 3} .2113111
{txt}{space 13}_Icowcode_435 {c |}{col 28}{res}{space 2}-.0383845{col 40}{space 2} .2290039{col 51}{space 1}   -0.17{col 60}{space 3}0.867{col 68}{space 4}-.4872674{col 81}{space 3} .4104983
{txt}{space 13}_Icowcode_436 {c |}{col 28}{res}{space 2}-.2326465{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.305{col 68}{space 4}-.6768934{col 81}{space 3} .2116004
{txt}{space 13}_Icowcode_437 {c |}{col 28}{res}{space 2}-.2329358{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.304{col 68}{space 4}-.6771827{col 81}{space 3} .2113111
{txt}{space 13}_Icowcode_438 {c |}{col 28}{res}{space 2}-.2329358{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.304{col 68}{space 4}-.6771827{col 81}{space 3} .2113111
{txt}{space 13}_Icowcode_439 {c |}{col 28}{res}{space 2}-.2292099{col 40}{space 2} .2266387{col 51}{space 1}   -1.01{col 60}{space 3}0.312{col 68}{space 4}-.6734567{col 81}{space 3}  .215037
{txt}{space 13}_Icowcode_450 {c |}{col 28}{res}{space 2}-.0342451{col 40}{space 2} .2291164{col 51}{space 1}   -0.15{col 60}{space 3}0.881{col 68}{space 4}-.4833485{col 81}{space 3} .4148582
{txt}{space 13}_Icowcode_451 {c |}{col 28}{res}{space 2}-.2329358{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.304{col 68}{space 4}-.6771827{col 81}{space 3} .2113111
{txt}{space 13}_Icowcode_452 {c |}{col 28}{res}{space 2}-.2316269{col 40}{space 2} .2266387{col 51}{space 1}   -1.02{col 60}{space 3}0.307{col 68}{space 4}-.6758738{col 81}{space 3} .2126199
{txt}{space 13}_Icowcode_461 {c |}{col 28}{res}{space 2}-.2326614{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.305{col 68}{space 4}-.6769083{col 81}{space 3} .2115854
{txt}{space 13}_Icowcode_471 {c |}{col 28}{res}{space 2}-.2325925{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.305{col 68}{space 4}-.6768393{col 81}{space 3} .2116544
{txt}{space 13}_Icowcode_475 {c |}{col 28}{res}{space 2}-.1264836{col 40}{space 2} .2267455{col 51}{space 1}   -0.56{col 60}{space 3}0.577{col 68}{space 4}-.5709396{col 81}{space 3} .3179725
{txt}{space 13}_Icowcode_481 {c |}{col 28}{res}{space 2}-.1810223{col 40}{space 2} .2253817{col 51}{space 1}   -0.80{col 60}{space 3}0.422{col 68}{space 4}-.6228053{col 81}{space 3} .2607606
{txt}{space 13}_Icowcode_482 {c |}{col 28}{res}{space 2} -.170845{col 40}{space 2} .2267663{col 51}{space 1}   -0.75{col 60}{space 3}0.451{col 68}{space 4}-.6153418{col 81}{space 3} .2736519
{txt}{space 13}_Icowcode_483 {c |}{col 28}{res}{space 2}-.1791238{col 40}{space 2} .2267151{col 51}{space 1}   -0.79{col 60}{space 3}0.429{col 68}{space 4}-.6235203{col 81}{space 3} .2652728
{txt}{space 13}_Icowcode_484 {c |}{col 28}{res}{space 2}-.1832631{col 40}{space 2} .2266934{col 51}{space 1}   -0.81{col 60}{space 3}0.419{col 68}{space 4}-.6276173{col 81}{space 3}  .261091
{txt}{space 13}_Icowcode_490 {c |}{col 28}{res}{space 2}-.1373952{col 40}{space 2} .2270759{col 51}{space 1}   -0.61{col 60}{space 3}0.545{col 68}{space 4}-.5824989{col 81}{space 3} .3077086
{txt}{space 13}_Icowcode_500 {c |}{col 28}{res}{space 2}-.1377299{col 40}{space 2} .2270759{col 51}{space 1}   -0.61{col 60}{space 3}0.544{col 68}{space 4}-.5828336{col 81}{space 3} .3073739
{txt}{space 13}_Icowcode_501 {c |}{col 28}{res}{space 2}-.2328516{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.304{col 68}{space 4}-.6770985{col 81}{space 3} .2113953
{txt}{space 13}_Icowcode_510 {c |}{col 28}{res}{space 2}-.2329358{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.304{col 68}{space 4}-.6771827{col 81}{space 3} .2113111
{txt}{space 13}_Icowcode_516 {c |}{col 28}{res}{space 2}-.1667056{col 40}{space 2} .2267958{col 51}{space 1}   -0.74{col 60}{space 3}0.462{col 68}{space 4}-.6112603{col 81}{space 3} .2778491
{txt}{space 13}_Icowcode_517 {c |}{col 28}{res}{space 2}-.1791238{col 40}{space 2} .2267151{col 51}{space 1}   -0.79{col 60}{space 3}0.429{col 68}{space 4}-.6235203{col 81}{space 3} .2652728
{txt}{space 13}_Icowcode_520 {c |}{col 28}{res}{space 2}-.2329358{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.304{col 68}{space 4}-.6771827{col 81}{space 3} .2113111
{txt}{space 13}_Icowcode_522 {c |}{col 28}{res}{space 2}-.2329358{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.304{col 68}{space 4}-.6771827{col 81}{space 3} .2113111
{txt}{space 13}_Icowcode_525 {c |}{col 28}{res}{space 2}-.2329358{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.304{col 68}{space 4}-.6771827{col 81}{space 3} .2113111
{txt}{space 13}_Icowcode_530 {c |}{col 28}{res}{space 2}-.1004754{col 40}{space 2} .2276241{col 51}{space 1}   -0.44{col 60}{space 3}0.659{col 68}{space 4}-.5466537{col 81}{space 3}  .345703
{txt}{space 13}_Icowcode_531 {c |}{col 28}{res}{space 2}        0{col 40}{txt}  (omitted)
{space 13}_Icowcode_540 {c |}{col 28}{res}{space 2}-.1545986{col 40}{space 2} .2246716{col 51}{space 1}   -0.69{col 60}{space 3}0.491{col 68}{space 4}-.5949896{col 81}{space 3} .2857923
{txt}{space 13}_Icowcode_541 {c |}{col 28}{res}{space 2}-.1211723{col 40}{space 2} .2272935{col 51}{space 1}   -0.53{col 60}{space 3}0.594{col 68}{space 4}-.5667026{col 81}{space 3} .3243579
{txt}{space 13}_Icowcode_551 {c |}{col 28}{res}{space 2}-.1335905{col 40}{space 2} .2271264{col 51}{space 1}   -0.59{col 60}{space 3}0.556{col 68}{space 4}-.5787932{col 81}{space 3} .3116122
{txt}{space 13}_Icowcode_552 {c |}{col 28}{res}{space 2}-.0252168{col 40}{space 2}  .229349{col 51}{space 1}   -0.11{col 60}{space 3}0.912{col 68}{space 4}-.4747762{col 81}{space 3} .4243425
{txt}{space 13}_Icowcode_553 {c |}{col 28}{res}{space 2}-.1381389{col 40}{space 2} .2269152{col 51}{space 1}   -0.61{col 60}{space 3}0.543{col 68}{space 4}-.5829277{col 81}{space 3} .3066498
{txt}{space 13}_Icowcode_560 {c |}{col 28}{res}{space 2}-.2217073{col 40}{space 2} .2262915{col 51}{space 1}   -0.98{col 60}{space 3}0.327{col 68}{space 4}-.6652736{col 81}{space 3}  .221859
{txt}{space 13}_Icowcode_565 {c |}{col 28}{res}{space 2}-.2329358{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.304{col 68}{space 4}-.6771827{col 81}{space 3} .2113111
{txt}{space 13}_Icowcode_570 {c |}{col 28}{res}{space 2}-.2329358{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.304{col 68}{space 4}-.6771827{col 81}{space 3} .2113111
{txt}{space 13}_Icowcode_571 {c |}{col 28}{res}{space 2}-.2142841{col 40}{space 2} .2260722{col 51}{space 1}   -0.95{col 60}{space 3}0.343{col 68}{space 4}-.6574205{col 81}{space 3} .2288523
{txt}{space 13}_Icowcode_580 {c |}{col 28}{res}{space 2}-.1508683{col 40}{space 2} .2246051{col 51}{space 1}   -0.67{col 60}{space 3}0.502{col 68}{space 4}-.5911289{col 81}{space 3} .2893923
{txt}{space 13}_Icowcode_581 {c |}{col 28}{res}{space 2}-.0837221{col 40}{space 2} .2238358{col 51}{space 1}   -0.37{col 60}{space 3}0.708{col 68}{space 4}-.5224749{col 81}{space 3} .3550306
{txt}{space 13}_Icowcode_590 {c |}{col 28}{res}{space 2}-.0316408{col 40}{space 2} .2237552{col 51}{space 1}   -0.14{col 60}{space 3}0.888{col 68}{space 4}-.4702356{col 81}{space 3} .4069539
{txt}{space 13}_Icowcode_591 {c |}{col 28}{res}{space 2}-.0576098{col 40}{space 2} .2237565{col 51}{space 1}   -0.26{col 60}{space 3}0.797{col 68}{space 4} -.496207{col 81}{space 3} .3809875
{txt}{space 13}_Icowcode_600 {c |}{col 28}{res}{space 2} -.138505{col 40}{space 2}  .227028{col 51}{space 1}   -0.61{col 60}{space 3}0.542{col 68}{space 4}-.5835149{col 81}{space 3} .3065049
{txt}{space 13}_Icowcode_615 {c |}{col 28}{res}{space 2}-.2257598{col 40}{space 2} .2266387{col 51}{space 1}   -1.00{col 60}{space 3}0.319{col 68}{space 4}-.6700067{col 81}{space 3} .2184871
{txt}{space 13}_Icowcode_616 {c |}{col 28}{res}{space 2}-.1018287{col 40}{space 2} .2265436{col 51}{space 1}   -0.45{col 60}{space 3}0.653{col 68}{space 4}-.5458891{col 81}{space 3} .3422318
{txt}{space 13}_Icowcode_620 {c |}{col 28}{res}{space 2}-.2329358{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.304{col 68}{space 4}-.6771827{col 81}{space 3} .2113111
{txt}{space 13}_Icowcode_625 {c |}{col 28}{res}{space 2}-.1990003{col 40}{space 2} .2256629{col 51}{space 1}   -0.88{col 60}{space 3}0.378{col 68}{space 4}-.6413344{col 81}{space 3} .2433339
{txt}{space 13}_Icowcode_630 {c |}{col 28}{res}{space 2} .1289424{col 40}{space 2} .2266387{col 51}{space 1}    0.57{col 60}{space 3}0.569{col 68}{space 4}-.3153045{col 81}{space 3} .5731892
{txt}{space 13}_Icowcode_640 {c |}{col 28}{res}{space 2}-.1492313{col 40}{space 2} .2247406{col 51}{space 1}   -0.66{col 60}{space 3}0.507{col 68}{space 4}-.5897575{col 81}{space 3} .2912949
{txt}{space 13}_Icowcode_645 {c |}{col 28}{res}{space 2}-.0430664{col 40}{space 2} .2237552{col 51}{space 1}   -0.19{col 60}{space 3}0.847{col 68}{space 4}-.4816611{col 81}{space 3} .3955284
{txt}{space 13}_Icowcode_651 {c |}{col 28}{res}{space 2} -.229901{col 40}{space 2} .2266387{col 51}{space 1}   -1.01{col 60}{space 3}0.310{col 68}{space 4}-.6741478{col 81}{space 3} .2143459
{txt}{space 13}_Icowcode_652 {c |}{col 28}{res}{space 2}-.2329358{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.304{col 68}{space 4}-.6771827{col 81}{space 3} .2113111
{txt}{space 13}_Icowcode_660 {c |}{col 28}{res}{space 2}-.1164668{col 40}{space 2} .2254443{col 51}{space 1}   -0.52{col 60}{space 3}0.605{col 68}{space 4}-.5583724{col 81}{space 3} .3254389
{txt}{space 13}_Icowcode_663 {c |}{col 28}{res}{space 2}-.1319992{col 40}{space 2}   .22431{col 51}{space 1}   -0.59{col 60}{space 3}0.556{col 68}{space 4}-.5716814{col 81}{space 3}  .307683
{txt}{space 13}_Icowcode_666 {c |}{col 28}{res}{space 2}-.0455945{col 40}{space 2} .2237552{col 51}{space 1}   -0.20{col 60}{space 3}0.839{col 68}{space 4}-.4841893{col 81}{space 3} .3930002
{txt}{space 13}_Icowcode_670 {c |}{col 28}{res}{space 2}-.1465599{col 40}{space 2} .2245411{col 51}{space 1}   -0.65{col 60}{space 3}0.514{col 68}{space 4}-.5866951{col 81}{space 3} .2935753
{txt}{space 13}_Icowcode_679 {c |}{col 28}{res}{space 2}-.2329358{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.304{col 68}{space 4}-.6771827{col 81}{space 3} .2113111
{txt}{space 13}_Icowcode_690 {c |}{col 28}{res}{space 2}-.0688008{col 40}{space 2} .2237754{col 51}{space 1}   -0.31{col 60}{space 3}0.759{col 68}{space 4} -.507435{col 81}{space 3} .3698335
{txt}{space 13}_Icowcode_692 {c |}{col 28}{res}{space 2}  -.06733{col 40}{space 2} .2239076{col 51}{space 1}   -0.30{col 60}{space 3}0.764{col 68}{space 4}-.5062234{col 81}{space 3} .3715634
{txt}{space 13}_Icowcode_694 {c |}{col 28}{res}{space 2} -.084687{col 40}{space 2} .2259958{col 51}{space 1}   -0.37{col 60}{space 3}0.708{col 68}{space 4}-.5276736{col 81}{space 3} .3582996
{txt}{space 13}_Icowcode_696 {c |}{col 28}{res}{space 2}-.0435169{col 40}{space 2} .2287865{col 51}{space 1}   -0.19{col 60}{space 3}0.849{col 68}{space 4}-.4919737{col 81}{space 3} .4049399
{txt}{space 13}_Icowcode_698 {c |}{col 28}{res}{space 2}-.0685385{col 40}{space 2} .2260982{col 51}{space 1}   -0.30{col 60}{space 3}0.762{col 68}{space 4}-.5117257{col 81}{space 3} .3746488
{txt}{space 13}_Icowcode_700 {c |}{col 28}{res}{space 2}-.0420479{col 40}{space 2} .2237666{col 51}{space 1}   -0.19{col 60}{space 3}0.851{col 68}{space 4}-.4806648{col 81}{space 3} .3965691
{txt}{space 13}_Icowcode_701 {c |}{col 28}{res}{space 2}-.2329358{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.304{col 68}{space 4}-.6771827{col 81}{space 3} .2113111
{txt}{space 13}_Icowcode_702 {c |}{col 28}{res}{space 2}-.0623049{col 40}{space 2} .2255366{col 51}{space 1}   -0.28{col 60}{space 3}0.782{col 68}{space 4}-.5043915{col 81}{space 3} .3797817
{txt}{space 13}_Icowcode_703 {c |}{col 28}{res}{space 2}-.2329358{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.304{col 68}{space 4}-.6771827{col 81}{space 3} .2113111
{txt}{space 13}_Icowcode_704 {c |}{col 28}{res}{space 2}  -.12397{col 40}{space 2} .2261979{col 51}{space 1}   -0.55{col 60}{space 3}0.584{col 68}{space 4}-.5673527{col 81}{space 3} .3194128
{txt}{space 13}_Icowcode_705 {c |}{col 28}{res}{space 2}-.1767438{col 40}{space 2} .2251227{col 51}{space 1}   -0.79{col 60}{space 3}0.432{col 68}{space 4}-.6180191{col 81}{space 3} .2645315
{txt}{space 13}_Icowcode_710 {c |}{col 28}{res}{space 2}-.1972962{col 40}{space 2} .2266387{col 51}{space 1}   -0.87{col 60}{space 3}0.384{col 68}{space 4}-.6415431{col 81}{space 3} .2469507
{txt}{space 13}_Icowcode_712 {c |}{col 28}{res}{space 2}-.0501491{col 40}{space 2} .2237565{col 51}{space 1}   -0.22{col 60}{space 3}0.823{col 68}{space 4}-.4887463{col 81}{space 3} .3884481
{txt}{space 13}_Icowcode_713 {c |}{col 28}{res}{space 2}        0{col 40}{txt}  (omitted)
{space 13}_Icowcode_715 {c |}{col 28}{res}{space 2}-.2248019{col 40}{space 2} .2266387{col 51}{space 1}   -0.99{col 60}{space 3}0.321{col 68}{space 4}-.6690487{col 81}{space 3}  .219445
{txt}{space 13}_Icowcode_716 {c |}{col 28}{res}{space 2}-.2329358{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.304{col 68}{space 4}-.6771827{col 81}{space 3} .2113111
{txt}{space 13}_Icowcode_731 {c |}{col 28}{res}{space 2}-.2329358{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.304{col 68}{space 4}-.6771827{col 81}{space 3} .2113111
{txt}{space 13}_Icowcode_732 {c |}{col 28}{res}{space 2}-.0917927{col 40}{space 2} .2434669{col 51}{space 1}   -0.38{col 60}{space 3}0.706{col 68}{space 4}-.5690254{col 81}{space 3}   .38544
{txt}{space 13}_Icowcode_740 {c |}{col 28}{res}{space 2}-.0204798{col 40}{space 2} .2443788{col 51}{space 1}   -0.08{col 60}{space 3}0.933{col 68}{space 4}   -.4995{col 81}{space 3} .4585405
{txt}{space 13}_Icowcode_750 {c |}{col 28}{res}{space 2}-.1334577{col 40}{space 2} .2271264{col 51}{space 1}   -0.59{col 60}{space 3}0.557{col 68}{space 4}-.5786603{col 81}{space 3}  .311745
{txt}{space 13}_Icowcode_760 {c |}{col 28}{res}{space 2}-.2329358{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.304{col 68}{space 4}-.6771827{col 81}{space 3} .2113111
{txt}{space 13}_Icowcode_770 {c |}{col 28}{res}{space 2}-.2323442{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.305{col 68}{space 4}-.6765911{col 81}{space 3} .2119026
{txt}{space 13}_Icowcode_771 {c |}{col 28}{res}{space 2}-.1911931{col 40}{space 2}  .226658{col 51}{space 1}   -0.84{col 60}{space 3}0.399{col 68}{space 4}-.6354778{col 81}{space 3} .2530916
{txt}{space 13}_Icowcode_775 {c |}{col 28}{res}{space 2}-.0216455{col 40}{space 2} .2253756{col 51}{space 1}   -0.10{col 60}{space 3}0.923{col 68}{space 4}-.4634164{col 81}{space 3} .4201254
{txt}{space 13}_Icowcode_780 {c |}{col 28}{res}{space 2}-.2329358{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.304{col 68}{space 4}-.6771827{col 81}{space 3} .2113111
{txt}{space 13}_Icowcode_781 {c |}{col 28}{res}{space 2}-.1434076{col 40}{space 2} .2244796{col 51}{space 1}   -0.64{col 60}{space 3}0.523{col 68}{space 4}-.5834222{col 81}{space 3}  .296607
{txt}{space 13}_Icowcode_790 {c |}{col 28}{res}{space 2}-.1620593{col 40}{space 2}  .224812{col 51}{space 1}   -0.72{col 60}{space 3}0.471{col 68}{space 4}-.6027256{col 81}{space 3}  .278607
{txt}{space 13}_Icowcode_800 {c |}{col 28}{res}{space 2}-.1205488{col 40}{space 2} .2272935{col 51}{space 1}   -0.53{col 60}{space 3}0.596{col 68}{space 4} -.566079{col 81}{space 3} .3249815
{txt}{space 13}_Icowcode_811 {c |}{col 28}{res}{space 2}-.2329358{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.304{col 68}{space 4}-.6771827{col 81}{space 3} .2113111
{txt}{space 13}_Icowcode_812 {c |}{col 28}{res}{space 2}-.2329358{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.304{col 68}{space 4}-.6771827{col 81}{space 3} .2113111
{txt}{space 13}_Icowcode_817 {c |}{col 28}{res}{space 2}-.0718109{col 40}{space 2} .2250745{col 51}{space 1}   -0.32{col 60}{space 3}0.750{col 68}{space 4}-.5129916{col 81}{space 3} .3693699
{txt}{space 13}_Icowcode_820 {c |}{col 28}{res}{space 2} .0595575{col 40}{space 2} .2468082{col 51}{space 1}    0.24{col 60}{space 3}0.809{col 68}{space 4}-.4242247{col 81}{space 3} .5433397
{txt}{space 13}_Icowcode_830 {c |}{col 28}{res}{space 2}-.0335839{col 40}{space 2} .2237754{col 51}{space 1}   -0.15{col 60}{space 3}0.881{col 68}{space 4}-.4722182{col 81}{space 3} .4050503
{txt}{space 13}_Icowcode_835 {c |}{col 28}{res}{space 2}-.0515843{col 40}{space 2} .2463266{col 51}{space 1}   -0.21{col 60}{space 3}0.834{col 68}{space 4}-.5344225{col 81}{space 3} .4312538
{txt}{space 13}_Icowcode_840 {c |}{col 28}{res}{space 2}-.0264439{col 40}{space 2} .2238006{col 51}{space 1}   -0.12{col 60}{space 3}0.906{col 68}{space 4}-.4651275{col 81}{space 3} .4122397
{txt}{space 13}_Icowcode_850 {c |}{col 28}{res}{space 2}-.0505396{col 40}{space 2} .2237552{col 51}{space 1}   -0.23{col 60}{space 3}0.821{col 68}{space 4}-.4891344{col 81}{space 3} .3880551
{txt}{space 13}_Icowcode_860 {c |}{col 28}{res}{space 2} .1373506{col 40}{space 2} .2267858{col 51}{space 1}    0.61{col 60}{space 3}0.545{col 68}{space 4}-.3071845{col 81}{space 3} .5818857
{txt}{space 13}_Icowcode_900 {c |}{col 28}{res}{space 2}-.0446907{col 40}{space 2} .2237565{col 51}{space 1}   -0.20{col 60}{space 3}0.842{col 68}{space 4} -.483288{col 81}{space 3} .3939065
{txt}{space 13}_Icowcode_910 {c |}{col 28}{res}{space 2} .1244206{col 40}{space 2} .2493855{col 51}{space 1}    0.50{col 60}{space 3}0.618{col 68}{space 4}-.3644134{col 81}{space 3} .6132545
{txt}{space 13}_Icowcode_920 {c |}{col 28}{res}{space 2}-.0538794{col 40}{space 2} .2237552{col 51}{space 1}   -0.24{col 60}{space 3}0.810{col 68}{space 4}-.4924742{col 81}{space 3} .3847153
{txt}{space 13}_Icowcode_935 {c |}{col 28}{res}{space 2}-.0799918{col 40}{space 2} .2238169{col 51}{space 1}   -0.36{col 60}{space 3}0.721{col 68}{space 4}-.5187075{col 81}{space 3} .3587239
{txt}{space 13}_Icowcode_940 {c |}{col 28}{res}{space 2}-.1396773{col 40}{space 2} .2244206{col 51}{space 1}   -0.62{col 60}{space 3}0.534{col 68}{space 4}-.5795762{col 81}{space 3} .3002216
{txt}{space 13}_Icowcode_946 {c |}{col 28}{res}{space 2}-.2329358{col 40}{space 2} .2266387{col 51}{space 1}   -1.03{col 60}{space 3}0.304{col 68}{space 4}-.6771827{col 81}{space 3} .2113111
{txt}{space 13}_Icowcode_947 {c |}{col 28}{res}{space 2}-.0576098{col 40}{space 2} .2237565{col 51}{space 1}   -0.26{col 60}{space 3}0.797{col 68}{space 4} -.496207{col 81}{space 3} .3809875
{txt}{space 13}_Icowcode_950 {c |}{col 28}{res}{space 2}-.0538794{col 40}{space 2} .2237552{col 51}{space 1}   -0.24{col 60}{space 3}0.810{col 68}{space 4}-.4924742{col 81}{space 3} .3847153
{txt}{space 13}_Icowcode_955 {c |}{col 28}{res}{space 2}-.0576098{col 40}{space 2} .2237565{col 51}{space 1}   -0.26{col 60}{space 3}0.797{col 68}{space 4} -.496207{col 81}{space 3} .3809875
{txt}{space 13}_Icowcode_970 {c |}{col 28}{res}{space 2}-.1359469{col 40}{space 2}  .224364{col 51}{space 1}   -0.61{col 60}{space 3}0.545{col 68}{space 4} -.575735{col 81}{space 3} .3038412
{txt}{space 13}_Icowcode_983 {c |}{col 28}{res}{space 2}-.0203063{col 40}{space 2} .2238572{col 51}{space 1}   -0.09{col 60}{space 3}0.928{col 68}{space 4} -.459101{col 81}{space 3} .4184883
{txt}{space 13}_Icowcode_986 {c |}{col 28}{res}{space 2}-.0501491{col 40}{space 2} .2237565{col 51}{space 1}   -0.22{col 60}{space 3}0.823{col 68}{space 4}-.4887463{col 81}{space 3} .3884481
{txt}{space 13}_Icowcode_987 {c |}{col 28}{res}{space 2}-.1253117{col 40}{space 2} .2272352{col 51}{space 1}   -0.55{col 60}{space 3}0.581{col 68}{space 4}-.5707276{col 81}{space 3} .3201043
{txt}{space 13}_Icowcode_990 {c |}{col 28}{res}{space 2}-.1172952{col 40}{space 2}  .224119{col 51}{space 1}   -0.52{col 60}{space 3}0.601{col 68}{space 4}-.5566029{col 81}{space 3} .3220125
{txt}{space 12}_Icowcode_1013 {c |}{col 28}{res}{space 2} -.038958{col 40}{space 2} .2237754{col 51}{space 1}   -0.17{col 60}{space 3}0.862{col 68}{space 4}-.4775923{col 81}{space 3} .3996762
{txt}{space 12}_Icowcode_1015 {c |}{col 28}{res}{space 2}-.1077534{col 40}{space 2} .2280214{col 51}{space 1}   -0.47{col 60}{space 3}0.637{col 68}{space 4}-.5547104{col 81}{space 3} .3392036
{txt}{space 12}_Icowcode_1020 {c |}{col 28}{res}{space 2}-.1424602{col 40}{space 2} .2244796{col 51}{space 1}   -0.63{col 60}{space 3}0.526{col 68}{space 4}-.5824747{col 81}{space 3} .2975544
{txt}{space 12}_Icowcode_1027 {c |}{col 28}{res}{space 2}        0{col 40}{txt}  (omitted)
{space 21}_cons {c |}{col 28}{res}{space 2}-.0852942{col 40}{space 2} .1877003{col 51}{space 1}   -0.45{col 60}{space 3}0.650{col 68}{space 4}-.4532157{col 81}{space 3} .2826274
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est3{txt} stored)

{com}. eststo: xi: regress deaths_7_day_pc L14be_cat_1_China_carryover L14be_cat_2_China_carryover L14be_cat_3_China_carryover  time time_sq time_cube i.cowcode
{txt}i.cowcode{col 19}_Icowcode_2-1027{col 39}(naturally coded; _Icowcode_2 omitted)
note: _Icowcode_531 omitted because of collinearity
note: _Icowcode_713 omitted because of collinearity
note: _Icowcode_1027 omitted because of collinearity

      Source {c |}       SS           df       MS      Number of obs   ={res}    11,328
{txt}{hline 13}{c +}{hline 34}   F(197, 11130)   = {res}    19.45
{txt}       Model {c |} {res} 6859.23123       197  34.8184327   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 19926.5165    11,130  1.79034291   {txt}R-squared       ={res}    0.2561
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.2429
{txt}       Total {c |} {res} 26785.7478    11,327  2.36476982   {txt}Root MSE        =   {res}  1.338

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            deaths_7_day_pc{col 29}{c |}      Coef.{col 41}   Std. Err.{col 53}      t{col 61}   P>|t|{col 69}     [95% Con{col 82}f. Interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
L14be_cat_1_China_carryover {c |}{col 29}{res}{space 2}-.2488012{col 41}{space 2}  .057373{col 52}{space 1}   -4.34{col 61}{space 3}0.000{col 69}{space 4}-.3612624{col 82}{space 3}  -.13634
{txt}L14be_cat_2_China_carryover {c |}{col 29}{res}{space 2}-.1355159{col 41}{space 2} .1556979{col 52}{space 1}   -0.87{col 61}{space 3}0.384{col 69}{space 4}-.4407114{col 82}{space 3} .1696797
{txt}L14be_cat_3_China_carryover {c |}{col 29}{res}{space 2}-.3019868{col 41}{space 2} .0603977{col 52}{space 1}   -5.00{col 61}{space 3}0.000{col 69}{space 4}-.4203771{col 82}{space 3}-.1835966
{txt}{space 23}time {c |}{col 29}{res}{space 2}  .033877{col 41}{space 2} .0177241{col 52}{space 1}    1.91{col 61}{space 3}0.056{col 69}{space 4}-.0008652{col 82}{space 3} .0686193
{txt}{space 20}time_sq {c |}{col 29}{res}{space 2}-.0010376{col 41}{space 2} .0004315{col 52}{space 1}   -2.40{col 61}{space 3}0.016{col 69}{space 4}-.0018834{col 82}{space 3}-.0001917
{txt}{space 18}time_cube {c |}{col 29}{res}{space 2} .0000107{col 41}{space 2} 3.25e-06{col 52}{space 1}    3.28{col 61}{space 3}0.001{col 69}{space 4} 4.29e-06{col 82}{space 3}  .000017
{txt}{space 15}_Icowcode_20 {c |}{col 29}{res}{space 2}-.2192505{col 41}{space 2} .2495579{col 52}{space 1}   -0.88{col 61}{space 3}0.380{col 69}{space 4}-.7084282{col 82}{space 3} .2699273
{txt}{space 15}_Icowcode_31 {c |}{col 29}{res}{space 2} -.047621{col 41}{space 2} .2463698{col 52}{space 1}   -0.19{col 61}{space 3}0.847{col 69}{space 4}-.5305495{col 82}{space 3} .4353076
{txt}{space 15}_Icowcode_40 {c |}{col 29}{res}{space 2}-.2312456{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.354{col 69}{space 4}-.7204234{col 82}{space 3} .2579321
{txt}{space 15}_Icowcode_41 {c |}{col 29}{res}{space 2}-.2120828{col 41}{space 2} .2488274{col 52}{space 1}   -0.85{col 61}{space 3}0.394{col 69}{space 4}-.6998287{col 82}{space 3} .2756631
{txt}{space 15}_Icowcode_42 {c |}{col 29}{res}{space 2}-.2208642{col 41}{space 2} .2495579{col 52}{space 1}   -0.89{col 61}{space 3}0.376{col 69}{space 4}-.7100419{col 82}{space 3} .2683135
{txt}{space 15}_Icowcode_51 {c |}{col 29}{res}{space 2}-.0460628{col 41}{space 2} .2463602{col 52}{space 1}   -0.19{col 61}{space 3}0.852{col 69}{space 4}-.5289726{col 82}{space 3} .4368469
{txt}{space 15}_Icowcode_52 {c |}{col 29}{res}{space 2}-.1587276{col 41}{space 2} .2475492{col 52}{space 1}   -0.64{col 61}{space 3}0.521{col 69}{space 4}-.6439679{col 82}{space 3} .3265126
{txt}{space 15}_Icowcode_53 {c |}{col 29}{res}{space 2}-.2229308{col 41}{space 2} .2495361{col 52}{space 1}   -0.89{col 61}{space 3}0.372{col 69}{space 4}-.7120657{col 82}{space 3} .2662041
{txt}{space 15}_Icowcode_54 {c |}{col 29}{res}{space 2} .0125166{col 41}{space 2} .2536361{col 52}{space 1}    0.05{col 61}{space 3}0.961{col 69}{space 4}-.4846551{col 82}{space 3} .5096882
{txt}{space 15}_Icowcode_55 {c |}{col 29}{res}{space 2}-.0244429{col 41}{space 2} .2464568{col 52}{space 1}   -0.10{col 61}{space 3}0.921{col 69}{space 4}-.5075419{col 82}{space 3}  .458656
{txt}{space 15}_Icowcode_56 {c |}{col 29}{res}{space 2} .0080705{col 41}{space 2} .2468271{col 52}{space 1}    0.03{col 61}{space 3}0.974{col 69}{space 4}-.4757544{col 82}{space 3} .4918954
{txt}{space 15}_Icowcode_58 {c |}{col 29}{res}{space 2}-.0518379{col 41}{space 2} .2463602{col 52}{space 1}   -0.21{col 61}{space 3}0.833{col 69}{space 4}-.5347477{col 82}{space 3} .4310718
{txt}{space 15}_Icowcode_60 {c |}{col 29}{res}{space 2} -.018194{col 41}{space 2} .2526077{col 52}{space 1}   -0.07{col 61}{space 3}0.943{col 69}{space 4}-.5133499{col 82}{space 3}  .476962
{txt}{space 15}_Icowcode_70 {c |}{col 29}{res}{space 2}-.1607422{col 41}{space 2} .2497576{col 52}{space 1}   -0.64{col 61}{space 3}0.520{col 69}{space 4}-.6503114{col 82}{space 3} .3288269
{txt}{space 15}_Icowcode_80 {c |}{col 29}{res}{space 2}-.0855737{col 41}{space 2} .2464217{col 52}{space 1}   -0.35{col 61}{space 3}0.728{col 69}{space 4}-.5686038{col 82}{space 3} .3974564
{txt}{space 15}_Icowcode_90 {c |}{col 29}{res}{space 2}-.0508553{col 41}{space 2} .2463602{col 52}{space 1}   -0.21{col 61}{space 3}0.836{col 69}{space 4} -.533765{col 82}{space 3} .4320545
{txt}{space 15}_Icowcode_91 {c |}{col 29}{res}{space 2}-.1065049{col 41}{space 2} .2500341{col 52}{space 1}   -0.43{col 61}{space 3}0.670{col 69}{space 4}-.5966161{col 82}{space 3} .3836063
{txt}{space 15}_Icowcode_92 {c |}{col 29}{res}{space 2}-.0518379{col 41}{space 2} .2463602{col 52}{space 1}   -0.21{col 61}{space 3}0.833{col 69}{space 4}-.5347477{col 82}{space 3} .4310718
{txt}{space 15}_Icowcode_93 {c |}{col 29}{res}{space 2}-.2327931{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.351{col 69}{space 4}-.7219709{col 82}{space 3} .2563846
{txt}{space 15}_Icowcode_94 {c |}{col 29}{res}{space 2}-.1547293{col 41}{space 2} .2497576{col 52}{space 1}   -0.62{col 61}{space 3}0.536{col 69}{space 4}-.6442985{col 82}{space 3} .3348399
{txt}{space 15}_Icowcode_95 {c |}{col 29}{res}{space 2} .0004551{col 41}{space 2} .2524505{col 52}{space 1}    0.00{col 61}{space 3}0.999{col 69}{space 4}-.4943926{col 82}{space 3} .4953027
{txt}{space 14}_Icowcode_100 {c |}{col 29}{res}{space 2}-.2319972{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.353{col 69}{space 4}-.7211749{col 82}{space 3} .2571806
{txt}{space 14}_Icowcode_101 {c |}{col 29}{res}{space 2} -.151189{col 41}{space 2} .2498533{col 52}{space 1}   -0.61{col 61}{space 3}0.545{col 69}{space 4}-.6409457{col 82}{space 3} .3385676
{txt}{space 14}_Icowcode_110 {c |}{col 29}{res}{space 2}-.2011733{col 41}{space 2} .2495361{col 52}{space 1}   -0.81{col 61}{space 3}0.420{col 69}{space 4}-.6903082{col 82}{space 3} .2879616
{txt}{space 14}_Icowcode_115 {c |}{col 29}{res}{space 2}-.0729228{col 41}{space 2} .2463698{col 52}{space 1}   -0.30{col 61}{space 3}0.767{col 69}{space 4}-.5558513{col 82}{space 3} .4100057
{txt}{space 14}_Icowcode_130 {c |}{col 29}{res}{space 2}-.1331482{col 41}{space 2} .2498033{col 52}{space 1}   -0.53{col 61}{space 3}0.594{col 69}{space 4} -.622807{col 82}{space 3} .3565106
{txt}{space 14}_Icowcode_135 {c |}{col 29}{res}{space 2}-.2293074{col 41}{space 2} .2495579{col 52}{space 1}   -0.92{col 61}{space 3}0.358{col 69}{space 4}-.7184851{col 82}{space 3} .2598704
{txt}{space 14}_Icowcode_140 {c |}{col 29}{res}{space 2} -.229145{col 41}{space 2} .2495579{col 52}{space 1}   -0.92{col 61}{space 3}0.359{col 69}{space 4}-.7183228{col 82}{space 3} .2600327
{txt}{space 14}_Icowcode_145 {c |}{col 29}{res}{space 2}-.2331676{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223454{col 82}{space 3} .2560101
{txt}{space 14}_Icowcode_150 {c |}{col 29}{res}{space 2}-.1463997{col 41}{space 2} .2498533{col 52}{space 1}   -0.59{col 61}{space 3}0.558{col 69}{space 4}-.6361563{col 82}{space 3} .3433569
{txt}{space 14}_Icowcode_155 {c |}{col 29}{res}{space 2}-.2309699{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.355{col 69}{space 4}-.7201476{col 82}{space 3} .2582079
{txt}{space 14}_Icowcode_160 {c |}{col 29}{res}{space 2}-.2152003{col 41}{space 2} .2495314{col 52}{space 1}   -0.86{col 61}{space 3}0.388{col 69}{space 4}-.7043261{col 82}{space 3} .2739255
{txt}{space 14}_Icowcode_165 {c |}{col 29}{res}{space 2}-.2280492{col 41}{space 2} .2495449{col 52}{space 1}   -0.91{col 61}{space 3}0.361{col 69}{space 4}-.7172014{col 82}{space 3}  .261103
{txt}{space 14}_Icowcode_200 {c |}{col 29}{res}{space 2} .1086469{col 41}{space 2}  .251862{col 52}{space 1}    0.43{col 61}{space 3}0.666{col 69}{space 4}-.3850473{col 82}{space 3} .6023411
{txt}{space 14}_Icowcode_205 {c |}{col 29}{res}{space 2}-.1983452{col 41}{space 2} .2495579{col 52}{space 1}   -0.79{col 61}{space 3}0.427{col 69}{space 4}-.6875229{col 82}{space 3} .2908325
{txt}{space 14}_Icowcode_210 {c |}{col 29}{res}{space 2} .0685188{col 41}{space 2} .2495579{col 52}{space 1}    0.27{col 61}{space 3}0.784{col 69}{space 4}-.4206589{col 82}{space 3} .5576965
{txt}{space 14}_Icowcode_211 {c |}{col 29}{res}{space 2}-.0131307{col 41}{space 2} .2495579{col 52}{space 1}   -0.05{col 61}{space 3}0.958{col 69}{space 4}-.5023085{col 82}{space 3}  .476047
{txt}{space 14}_Icowcode_212 {c |}{col 29}{res}{space 2} .0217278{col 41}{space 2} .2495579{col 52}{space 1}    0.09{col 61}{space 3}0.931{col 69}{space 4}-.4674499{col 82}{space 3} .5109055
{txt}{space 14}_Icowcode_220 {c |}{col 29}{res}{space 2} .0643935{col 41}{space 2} .2495579{col 52}{space 1}    0.26{col 61}{space 3}0.796{col 69}{space 4}-.4247843{col 82}{space 3} .5535712
{txt}{space 14}_Icowcode_223 {c |}{col 29}{res}{space 2}-.2331676{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223454{col 82}{space 3} .2560101
{txt}{space 14}_Icowcode_225 {c |}{col 29}{res}{space 2} .0484053{col 41}{space 2} .2495579{col 52}{space 1}    0.19{col 61}{space 3}0.846{col 69}{space 4}-.4407724{col 82}{space 3} .5375831
{txt}{space 14}_Icowcode_230 {c |}{col 29}{res}{space 2} .8749212{col 41}{space 2} .2495579{col 52}{space 1}    3.51{col 61}{space 3}0.000{col 69}{space 4} .3857434{col 82}{space 3} 1.364099
{txt}{space 14}_Icowcode_235 {c |}{col 29}{res}{space 2}-.1717157{col 41}{space 2} .2495579{col 52}{space 1}   -0.69{col 61}{space 3}0.491{col 69}{space 4}-.6608934{col 82}{space 3} .3174621
{txt}{space 14}_Icowcode_255 {c |}{col 29}{res}{space 2} -.195989{col 41}{space 2} .2495579{col 52}{space 1}   -0.79{col 61}{space 3}0.432{col 69}{space 4}-.6851667{col 82}{space 3} .2931887
{txt}{space 14}_Icowcode_290 {c |}{col 29}{res}{space 2}-.2283218{col 41}{space 2} .2495579{col 52}{space 1}   -0.91{col 61}{space 3}0.360{col 69}{space 4}-.7174995{col 82}{space 3} .2608559
{txt}{space 14}_Icowcode_305 {c |}{col 29}{res}{space 2}-.1500592{col 41}{space 2} .2495348{col 52}{space 1}   -0.60{col 61}{space 3}0.548{col 69}{space 4}-.6391916{col 82}{space 3} .3390732
{txt}{space 14}_Icowcode_310 {c |}{col 29}{res}{space 2}-.2066461{col 41}{space 2} .2491085{col 52}{space 1}   -0.83{col 61}{space 3}0.407{col 69}{space 4}-.6949429{col 82}{space 3} .2816507
{txt}{space 14}_Icowcode_316 {c |}{col 29}{res}{space 2} -.226562{col 41}{space 2} .2495579{col 52}{space 1}   -0.91{col 61}{space 3}0.364{col 69}{space 4}-.7157397{col 82}{space 3} .2626158
{txt}{space 14}_Icowcode_317 {c |}{col 29}{res}{space 2}-.2327231{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.351{col 69}{space 4}-.7219008{col 82}{space 3} .2564546
{txt}{space 14}_Icowcode_325 {c |}{col 29}{res}{space 2} 1.692361{col 41}{space 2} .2495579{col 52}{space 1}    6.78{col 61}{space 3}0.000{col 69}{space 4} 1.203183{col 82}{space 3} 2.181539
{txt}{space 14}_Icowcode_331 {c |}{col 29}{res}{space 2} 10.08704{col 41}{space 2} .2495579{col 52}{space 1}   40.42{col 61}{space 3}0.000{col 69}{space 4} 9.597864{col 82}{space 3} 10.57622
{txt}{space 14}_Icowcode_338 {c |}{col 29}{res}{space 2}-.1909979{col 41}{space 2} .2481901{col 52}{space 1}   -0.77{col 61}{space 3}0.442{col 69}{space 4}-.6774945{col 82}{space 3} .2954986
{txt}{space 14}_Icowcode_339 {c |}{col 29}{res}{space 2}  -.12936{col 41}{space 2} .2498033{col 52}{space 1}   -0.52{col 61}{space 3}0.605{col 69}{space 4}-.6190188{col 82}{space 3} .3602988
{txt}{space 14}_Icowcode_341 {c |}{col 29}{res}{space 2}-.2045233{col 41}{space 2} .2497834{col 52}{space 1}   -0.82{col 61}{space 3}0.413{col 69}{space 4}-.6941431{col 82}{space 3} .2850965
{txt}{space 14}_Icowcode_343 {c |}{col 29}{res}{space 2}-.1349988{col 41}{space 2} .2498533{col 52}{space 1}   -0.54{col 61}{space 3}0.589{col 69}{space 4}-.6247554{col 82}{space 3} .3547579
{txt}{space 14}_Icowcode_344 {c |}{col 29}{res}{space 2} -.139639{col 41}{space 2} .2499073{col 52}{space 1}   -0.56{col 61}{space 3}0.576{col 69}{space 4}-.6295016{col 82}{space 3} .3502237
{txt}{space 14}_Icowcode_346 {c |}{col 29}{res}{space 2}-.1196687{col 41}{space 2} .2500694{col 52}{space 1}   -0.48{col 61}{space 3}0.632{col 69}{space 4}-.6098491{col 82}{space 3} .3705116
{txt}{space 14}_Icowcode_347 {c |}{col 29}{res}{space 2}-.2266069{col 41}{space 2} .2495579{col 52}{space 1}   -0.91{col 61}{space 3}0.364{col 69}{space 4}-.7157846{col 82}{space 3} .2625708
{txt}{space 14}_Icowcode_349 {c |}{col 29}{res}{space 2}-.1981421{col 41}{space 2} .2495579{col 52}{space 1}   -0.79{col 61}{space 3}0.427{col 69}{space 4}-.6873198{col 82}{space 3} .2910356
{txt}{space 14}_Icowcode_350 {c |}{col 29}{res}{space 2} -.201355{col 41}{space 2} .2495579{col 52}{space 1}   -0.81{col 61}{space 3}0.420{col 69}{space 4}-.6905328{col 82}{space 3} .2878227
{txt}{space 14}_Icowcode_352 {c |}{col 29}{res}{space 2}-.1238158{col 41}{space 2} .2498033{col 52}{space 1}   -0.50{col 61}{space 3}0.620{col 69}{space 4}-.6134746{col 82}{space 3}  .365843
{txt}{space 14}_Icowcode_355 {c |}{col 29}{res}{space 2}-.0211928{col 41}{space 2} .2522973{col 52}{space 1}   -0.08{col 61}{space 3}0.933{col 69}{space 4}-.5157401{col 82}{space 3} .4733546
{txt}{space 14}_Icowcode_359 {c |}{col 29}{res}{space 2}-.2091416{col 41}{space 2} .2489661{col 52}{space 1}   -0.84{col 61}{space 3}0.401{col 69}{space 4}-.6971592{col 82}{space 3} .2788761
{txt}{space 14}_Icowcode_360 {c |}{col 29}{res}{space 2} -.160925{col 41}{space 2} .2496787{col 52}{space 1}   -0.64{col 61}{space 3}0.519{col 69}{space 4}-.6503396{col 82}{space 3} .3284895
{txt}{space 14}_Icowcode_365 {c |}{col 29}{res}{space 2}-.0558203{col 41}{space 2} .2463545{col 52}{space 1}   -0.23{col 61}{space 3}0.821{col 69}{space 4}-.5387187{col 82}{space 3} .4270782
{txt}{space 14}_Icowcode_366 {c |}{col 29}{res}{space 2}-.2276729{col 41}{space 2} .2495579{col 52}{space 1}   -0.91{col 61}{space 3}0.362{col 69}{space 4}-.7168506{col 82}{space 3} .2615048
{txt}{space 14}_Icowcode_367 {c |}{col 29}{res}{space 2}-.2331676{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223454{col 82}{space 3} .2560101
{txt}{space 14}_Icowcode_368 {c |}{col 29}{res}{space 2}-.2176107{col 41}{space 2} .2495579{col 52}{space 1}   -0.87{col 61}{space 3}0.383{col 69}{space 4}-.7067884{col 82}{space 3} .2715671
{txt}{space 14}_Icowcode_369 {c |}{col 29}{res}{space 2}-.2317026{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.353{col 69}{space 4}-.7208803{col 82}{space 3} .2574752
{txt}{space 14}_Icowcode_370 {c |}{col 29}{res}{space 2}-.1410361{col 41}{space 2} .2499656{col 52}{space 1}   -0.56{col 61}{space 3}0.573{col 69}{space 4} -.631013{col 82}{space 3} .3489408
{txt}{space 14}_Icowcode_371 {c |}{col 29}{res}{space 2}-.2315271{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.354{col 69}{space 4}-.7207048{col 82}{space 3} .2576507
{txt}{space 14}_Icowcode_372 {c |}{col 29}{res}{space 2}-.2331676{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223454{col 82}{space 3} .2560101
{txt}{space 14}_Icowcode_373 {c |}{col 29}{res}{space 2}-.2302445{col 41}{space 2} .2495579{col 52}{space 1}   -0.92{col 61}{space 3}0.356{col 69}{space 4}-.7194222{col 82}{space 3} .2589333
{txt}{space 14}_Icowcode_375 {c |}{col 29}{res}{space 2}-.2248267{col 41}{space 2} .2495579{col 52}{space 1}   -0.90{col 61}{space 3}0.368{col 69}{space 4}-.7140044{col 82}{space 3} .2643511
{txt}{space 14}_Icowcode_380 {c |}{col 29}{res}{space 2}-.0442263{col 41}{space 2} .2495579{col 52}{space 1}   -0.18{col 61}{space 3}0.859{col 69}{space 4}-.5334041{col 82}{space 3} .4449514
{txt}{space 14}_Icowcode_385 {c |}{col 29}{res}{space 2}-.1953341{col 41}{space 2} .2495579{col 52}{space 1}   -0.78{col 61}{space 3}0.434{col 69}{space 4}-.6845118{col 82}{space 3} .2938436
{txt}{space 14}_Icowcode_390 {c |}{col 29}{res}{space 2}-.0844002{col 41}{space 2} .2496787{col 52}{space 1}   -0.34{col 61}{space 3}0.735{col 69}{space 4}-.5738147{col 82}{space 3} .4050144
{txt}{space 14}_Icowcode_395 {c |}{col 29}{res}{space 2}-.0706433{col 41}{space 2} .2499073{col 52}{space 1}   -0.28{col 61}{space 3}0.777{col 69}{space 4} -.560506{col 82}{space 3} .4192194
{txt}{space 14}_Icowcode_403 {c |}{col 29}{res}{space 2}-.2331676{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223454{col 82}{space 3} .2560101
{txt}{space 14}_Icowcode_404 {c |}{col 29}{res}{space 2}-.2331676{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223454{col 82}{space 3} .2560101
{txt}{space 14}_Icowcode_411 {c |}{col 29}{res}{space 2}-.1154134{col 41}{space 2} .2475467{col 52}{space 1}   -0.47{col 61}{space 3}0.641{col 69}{space 4}-.6006488{col 82}{space 3}  .369822
{txt}{space 14}_Icowcode_420 {c |}{col 29}{res}{space 2} -.227858{col 41}{space 2} .2495579{col 52}{space 1}   -0.91{col 61}{space 3}0.361{col 69}{space 4}-.7170357{col 82}{space 3} .2613197
{txt}{space 14}_Icowcode_432 {c |}{col 29}{res}{space 2}-.2331676{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223454{col 82}{space 3} .2560101
{txt}{space 14}_Icowcode_433 {c |}{col 29}{res}{space 2}-.2331676{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223454{col 82}{space 3} .2560101
{txt}{space 14}_Icowcode_434 {c |}{col 29}{res}{space 2}-.2331676{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223454{col 82}{space 3} .2560101
{txt}{space 14}_Icowcode_435 {c |}{col 29}{res}{space 2}-.0284308{col 41}{space 2} .2522973{col 52}{space 1}   -0.11{col 61}{space 3}0.910{col 69}{space 4}-.5229781{col 82}{space 3} .4661165
{txt}{space 14}_Icowcode_436 {c |}{col 29}{res}{space 2} -.232844{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.351{col 69}{space 4}-.7220217{col 82}{space 3} .2563338
{txt}{space 14}_Icowcode_437 {c |}{col 29}{res}{space 2}-.2331676{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223454{col 82}{space 3} .2560101
{txt}{space 14}_Icowcode_438 {c |}{col 29}{res}{space 2}-.2331676{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223454{col 82}{space 3} .2560101
{txt}{space 14}_Icowcode_439 {c |}{col 29}{res}{space 2}-.2289996{col 41}{space 2} .2495579{col 52}{space 1}   -0.92{col 61}{space 3}0.359{col 69}{space 4}-.7181774{col 82}{space 3} .2601781
{txt}{space 14}_Icowcode_450 {c |}{col 29}{res}{space 2}-.0233124{col 41}{space 2} .2524505{col 52}{space 1}   -0.09{col 61}{space 3}0.926{col 69}{space 4}  -.51816{col 82}{space 3} .4715353
{txt}{space 14}_Icowcode_451 {c |}{col 29}{res}{space 2}-.2331676{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223454{col 82}{space 3} .2560101
{txt}{space 14}_Icowcode_452 {c |}{col 29}{res}{space 2}-.2317035{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.353{col 69}{space 4}-.7208812{col 82}{space 3} .2574743
{txt}{space 14}_Icowcode_461 {c |}{col 29}{res}{space 2}-.2328607{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.351{col 69}{space 4}-.7220385{col 82}{space 3}  .256317
{txt}{space 14}_Icowcode_471 {c |}{col 29}{res}{space 2}-.2327836{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.351{col 69}{space 4}-.7219613{col 82}{space 3} .2563942
{txt}{space 14}_Icowcode_475 {c |}{col 29}{res}{space 2}-.1512111{col 41}{space 2} .2498533{col 52}{space 1}   -0.61{col 61}{space 3}0.545{col 69}{space 4}-.6409677{col 82}{space 3} .3385455
{txt}{space 14}_Icowcode_481 {c |}{col 29}{res}{space 2}-.2040852{col 41}{space 2} .2488274{col 52}{space 1}   -0.82{col 61}{space 3}0.412{col 69}{space 4} -.691831{col 82}{space 3} .2836607
{txt}{space 14}_Icowcode_482 {c |}{col 29}{res}{space 2}-.1922203{col 41}{space 2} .2495713{col 52}{space 1}   -0.77{col 61}{space 3}0.441{col 69}{space 4}-.6814242{col 82}{space 3} .2969837
{txt}{space 14}_Icowcode_483 {c |}{col 29}{res}{space 2}-.2024571{col 41}{space 2} .2495428{col 52}{space 1}   -0.81{col 61}{space 3}0.417{col 69}{space 4}-.6916051{col 82}{space 3} .2866909
{txt}{space 14}_Icowcode_484 {c |}{col 29}{res}{space 2}-.2075755{col 41}{space 2} .2495348{col 52}{space 1}   -0.83{col 61}{space 3}0.406{col 69}{space 4}-.6967079{col 82}{space 3} .2815569
{txt}{space 14}_Icowcode_490 {c |}{col 29}{res}{space 2}-.1508985{col 41}{space 2} .2498533{col 52}{space 1}   -0.60{col 61}{space 3}0.546{col 69}{space 4}-.6406551{col 82}{space 3} .3388582
{txt}{space 14}_Icowcode_500 {c |}{col 29}{res}{space 2}-.1512729{col 41}{space 2} .2498533{col 52}{space 1}   -0.61{col 61}{space 3}0.545{col 69}{space 4}-.6410295{col 82}{space 3} .3384837
{txt}{space 14}_Icowcode_501 {c |}{col 29}{res}{space 2}-.2330734{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7222511{col 82}{space 3} .2561043
{txt}{space 14}_Icowcode_510 {c |}{col 29}{res}{space 2}-.2331676{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223454{col 82}{space 3} .2560101
{txt}{space 14}_Icowcode_516 {c |}{col 29}{res}{space 2}-.1871018{col 41}{space 2} .2495919{col 52}{space 1}   -0.75{col 61}{space 3}0.453{col 69}{space 4}-.6763461{col 82}{space 3} .3021424
{txt}{space 14}_Icowcode_517 {c |}{col 29}{res}{space 2}-.2024571{col 41}{space 2} .2495428{col 52}{space 1}   -0.81{col 61}{space 3}0.417{col 69}{space 4}-.6916051{col 82}{space 3} .2866909
{txt}{space 14}_Icowcode_520 {c |}{col 29}{res}{space 2}-.2331676{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223454{col 82}{space 3} .2560101
{txt}{space 14}_Icowcode_522 {c |}{col 29}{res}{space 2}-.2331676{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223454{col 82}{space 3} .2560101
{txt}{space 14}_Icowcode_525 {c |}{col 29}{res}{space 2}-.2331676{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223454{col 82}{space 3} .2560101
{txt}{space 14}_Icowcode_530 {c |}{col 29}{res}{space 2}-.1052071{col 41}{space 2} .2504903{col 52}{space 1}   -0.42{col 61}{space 3}0.674{col 69}{space 4}-.5962125{col 82}{space 3} .3857983
{txt}{space 14}_Icowcode_531 {c |}{col 29}{res}{space 2}        0{col 41}{txt}  (omitted)
{space 14}_Icowcode_540 {c |}{col 29}{res}{space 2}-.1741301{col 41}{space 2} .2477477{col 52}{space 1}   -0.70{col 61}{space 3}0.482{col 69}{space 4}-.6597595{col 82}{space 3} .3114994
{txt}{space 14}_Icowcode_541 {c |}{col 29}{res}{space 2}-.1307992{col 41}{space 2} .2500947{col 52}{space 1}   -0.52{col 61}{space 3}0.601{col 69}{space 4}-.6210291{col 82}{space 3} .3594307
{txt}{space 14}_Icowcode_551 {c |}{col 29}{res}{space 2}-.1461545{col 41}{space 2} .2499073{col 52}{space 1}   -0.58{col 61}{space 3}0.559{col 69}{space 4}-.6360172{col 82}{space 3} .3437082
{txt}{space 14}_Icowcode_552 {c |}{col 29}{res}{space 2}-.0122371{col 41}{space 2} .2527691{col 52}{space 1}   -0.05{col 61}{space 3}0.961{col 69}{space 4}-.5077092{col 82}{space 3} .4832351
{txt}{space 14}_Icowcode_553 {c |}{col 29}{res}{space 2}-.1563913{col 41}{space 2} .2498033{col 52}{space 1}   -0.63{col 61}{space 3}0.531{col 69}{space 4}-.6460501{col 82}{space 3} .3332675
{txt}{space 14}_Icowcode_560 {c |}{col 29}{res}{space 2}-.2331257{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223035{col 82}{space 3}  .256052
{txt}{space 14}_Icowcode_565 {c |}{col 29}{res}{space 2}-.2331676{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223454{col 82}{space 3} .2560101
{txt}{space 14}_Icowcode_570 {c |}{col 29}{res}{space 2}-.2331676{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223454{col 82}{space 3} .2560101
{txt}{space 14}_Icowcode_571 {c |}{col 29}{res}{space 2}-.2331676{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223454{col 82}{space 3} .2560101
{txt}{space 14}_Icowcode_580 {c |}{col 29}{res}{space 2}-.1699131{col 41}{space 2} .2476466{col 52}{space 1}   -0.69{col 61}{space 3}0.493{col 69}{space 4}-.6553442{col 82}{space 3}  .315518
{txt}{space 14}_Icowcode_581 {c |}{col 29}{res}{space 2}-.0940076{col 41}{space 2} .2464754{col 52}{space 1}   -0.38{col 61}{space 3}0.703{col 69}{space 4} -.577143{col 82}{space 3} .3891277
{txt}{space 14}_Icowcode_590 {c |}{col 29}{res}{space 2}-.0353948{col 41}{space 2} .2463526{col 52}{space 1}   -0.14{col 61}{space 3}0.886{col 69}{space 4}-.5182895{col 82}{space 3} .4474998
{txt}{space 14}_Icowcode_591 {c |}{col 29}{res}{space 2}-.0644889{col 41}{space 2} .2463545{col 52}{space 1}   -0.26{col 61}{space 3}0.794{col 69}{space 4}-.5473873{col 82}{space 3} .4184096
{txt}{space 14}_Icowcode_600 {c |}{col 29}{res}{space 2}-.1526279{col 41}{space 2} .2498033{col 52}{space 1}   -0.61{col 61}{space 3}0.541{col 69}{space 4}-.6422867{col 82}{space 3} .3370309
{txt}{space 14}_Icowcode_615 {c |}{col 29}{res}{space 2}-.2251403{col 41}{space 2} .2495579{col 52}{space 1}   -0.90{col 61}{space 3}0.367{col 69}{space 4} -.714318{col 82}{space 3} .2640375
{txt}{space 14}_Icowcode_616 {c |}{col 29}{res}{space 2}-.1351739{col 41}{space 2} .2499656{col 52}{space 1}   -0.54{col 61}{space 3}0.589{col 69}{space 4}-.6251508{col 82}{space 3}  .354803
{txt}{space 14}_Icowcode_620 {c |}{col 29}{res}{space 2}-.2331676{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223454{col 82}{space 3} .2560101
{txt}{space 14}_Icowcode_625 {c |}{col 29}{res}{space 2}-.2243282{col 41}{space 2} .2492546{col 52}{space 1}   -0.90{col 61}{space 3}0.368{col 69}{space 4}-.7129114{col 82}{space 3}  .264255
{txt}{space 14}_Icowcode_630 {c |}{col 29}{res}{space 2} .1716452{col 41}{space 2} .2495579{col 52}{space 1}    0.69{col 61}{space 3}0.492{col 69}{space 4}-.3175325{col 82}{space 3}  .660823
{txt}{space 14}_Icowcode_640 {c |}{col 29}{res}{space 2}  -.16817{col 41}{space 2} .2478527{col 52}{space 1}   -0.68{col 61}{space 3}0.497{col 69}{space 4}-.6540051{col 82}{space 3} .3176652
{txt}{space 14}_Icowcode_645 {c |}{col 29}{res}{space 2}-.0481759{col 41}{space 2} .2463526{col 52}{space 1}   -0.20{col 61}{space 3}0.845{col 69}{space 4}-.5310706{col 82}{space 3} .4347187
{txt}{space 14}_Icowcode_651 {c |}{col 29}{res}{space 2}-.2297727{col 41}{space 2} .2495579{col 52}{space 1}   -0.92{col 61}{space 3}0.357{col 69}{space 4}-.7189505{col 82}{space 3}  .259405
{txt}{space 14}_Icowcode_652 {c |}{col 29}{res}{space 2}-.2331676{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223454{col 82}{space 3} .2560101
{txt}{space 14}_Icowcode_660 {c |}{col 29}{res}{space 2}-.1502953{col 41}{space 2} .2477742{col 52}{space 1}   -0.61{col 61}{space 3}0.544{col 69}{space 4}-.6359767{col 82}{space 3}  .335386
{txt}{space 14}_Icowcode_663 {c |}{col 29}{res}{space 2} -.148585{col 41}{space 2} .2471975{col 52}{space 1}   -0.60{col 61}{space 3}0.548{col 69}{space 4}-.6331359{col 82}{space 3} .3359658
{txt}{space 14}_Icowcode_666 {c |}{col 29}{res}{space 2} -.051004{col 41}{space 2} .2463526{col 52}{space 1}   -0.21{col 61}{space 3}0.836{col 69}{space 4}-.5338987{col 82}{space 3} .4318906
{txt}{space 14}_Icowcode_670 {c |}{col 29}{res}{space 2}-.1650495{col 41}{space 2} .2475492{col 52}{space 1}   -0.67{col 61}{space 3}0.505{col 69}{space 4}-.6502897{col 82}{space 3} .3201908
{txt}{space 14}_Icowcode_679 {c |}{col 29}{res}{space 2}-.2331676{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223454{col 82}{space 3} .2560101
{txt}{space 14}_Icowcode_690 {c |}{col 29}{res}{space 2}-.0771398{col 41}{space 2} .2463833{col 52}{space 1}   -0.31{col 61}{space 3}0.754{col 69}{space 4}-.5600946{col 82}{space 3} .4058151
{txt}{space 14}_Icowcode_692 {c |}{col 29}{res}{space 2}-.0758028{col 41}{space 2} .2465847{col 52}{space 1}   -0.31{col 61}{space 3}0.759{col 69}{space 4}-.5591525{col 82}{space 3} .4075468
{txt}{space 14}_Icowcode_694 {c |}{col 29}{res}{space 2}-.1199512{col 41}{space 2} .2492696{col 52}{space 1}   -0.48{col 61}{space 3}0.630{col 69}{space 4}-.6085637{col 82}{space 3} .3686613
{txt}{space 14}_Icowcode_696 {c |}{col 29}{res}{space 2}-.0351479{col 41}{space 2}  .252003{col 52}{space 1}   -0.14{col 61}{space 3}0.889{col 69}{space 4}-.5291185{col 82}{space 3} .4588227
{txt}{space 14}_Icowcode_698 {c |}{col 29}{res}{space 2} -.100379{col 41}{space 2} .2493487{col 52}{space 1}   -0.40{col 61}{space 3}0.687{col 69}{space 4}-.5891466{col 82}{space 3} .3883886
{txt}{space 14}_Icowcode_700 {c |}{col 29}{res}{space 2}-.0469045{col 41}{space 2} .2463698{col 52}{space 1}   -0.19{col 61}{space 3}0.849{col 69}{space 4} -.529833{col 82}{space 3} .4360241
{txt}{space 14}_Icowcode_701 {c |}{col 29}{res}{space 2}-.2331676{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223454{col 82}{space 3} .2560101
{txt}{space 14}_Icowcode_702 {c |}{col 29}{res}{space 2}-.0946494{col 41}{space 2} .2485573{col 52}{space 1}   -0.38{col 61}{space 3}0.703{col 69}{space 4}-.5818658{col 82}{space 3}  .392567
{txt}{space 14}_Icowcode_703 {c |}{col 29}{res}{space 2}-.2331676{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223454{col 82}{space 3} .2560101
{txt}{space 14}_Icowcode_704 {c |}{col 29}{res}{space 2}-.1665547{col 41}{space 2} .2497161{col 52}{space 1}   -0.67{col 61}{space 3}0.505{col 69}{space 4}-.6560425{col 82}{space 3} .3229331
{txt}{space 14}_Icowcode_705 {c |}{col 29}{res}{space 2}-.1991669{col 41}{space 2} .2484338{col 52}{space 1}   -0.80{col 61}{space 3}0.423{col 69}{space 4}-.6861411{col 82}{space 3} .2878074
{txt}{space 14}_Icowcode_710 {c |}{col 29}{res}{space 2}-.1939463{col 41}{space 2} .2495579{col 52}{space 1}   -0.78{col 61}{space 3}0.437{col 69}{space 4}-.6831241{col 82}{space 3} .2952314
{txt}{space 14}_Icowcode_712 {c |}{col 29}{res}{space 2}-.0560549{col 41}{space 2} .2463545{col 52}{space 1}   -0.23{col 61}{space 3}0.820{col 69}{space 4}-.5389534{col 82}{space 3} .4268435
{txt}{space 14}_Icowcode_713 {c |}{col 29}{res}{space 2}        0{col 41}{txt}  (omitted)
{space 14}_Icowcode_715 {c |}{col 29}{res}{space 2}-.2240686{col 41}{space 2} .2495579{col 52}{space 1}   -0.90{col 61}{space 3}0.369{col 69}{space 4}-.7132464{col 82}{space 3} .2651091
{txt}{space 14}_Icowcode_716 {c |}{col 29}{res}{space 2}-.2331676{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223454{col 82}{space 3} .2560101
{txt}{space 14}_Icowcode_731 {c |}{col 29}{res}{space 2}-.2331676{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223454{col 82}{space 3} .2560101
{txt}{space 14}_Icowcode_732 {c |}{col 29}{res}{space 2}-.1041778{col 41}{space 2} .2699687{col 52}{space 1}   -0.39{col 61}{space 3}0.700{col 69}{space 4}-.6333642{col 82}{space 3} .4250086
{txt}{space 14}_Icowcode_740 {c |}{col 29}{res}{space 2}-.0842043{col 41}{space 2} .2723246{col 52}{space 1}   -0.31{col 61}{space 3}0.757{col 69}{space 4}-.6180089{col 82}{space 3} .4496002
{txt}{space 14}_Icowcode_750 {c |}{col 29}{res}{space 2}-.1460059{col 41}{space 2} .2499073{col 52}{space 1}   -0.58{col 61}{space 3}0.559{col 69}{space 4}-.6358686{col 82}{space 3} .3438568
{txt}{space 14}_Icowcode_760 {c |}{col 29}{res}{space 2}-.2331676{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223454{col 82}{space 3} .2560101
{txt}{space 14}_Icowcode_770 {c |}{col 29}{res}{space 2}-.2325059{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.352{col 69}{space 4}-.7216836{col 82}{space 3} .2566719
{txt}{space 14}_Icowcode_771 {c |}{col 29}{res}{space 2}-.2174222{col 41}{space 2} .2495314{col 52}{space 1}   -0.87{col 61}{space 3}0.384{col 69}{space 4}-.7065481{col 82}{space 3} .2717036
{txt}{space 14}_Icowcode_775 {c |}{col 29}{res}{space 2}-.0441937{col 41}{space 2} .2470442{col 52}{space 1}   -0.18{col 61}{space 3}0.858{col 69}{space 4}-.5284441{col 82}{space 3} .4400566
{txt}{space 14}_Icowcode_780 {c |}{col 29}{res}{space 2}-.2331676{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223454{col 82}{space 3} .2560101
{txt}{space 14}_Icowcode_781 {c |}{col 29}{res}{space 2}-.1614792{col 41}{space 2} .2474556{col 52}{space 1}   -0.65{col 61}{space 3}0.514{col 69}{space 4}-.6465359{col 82}{space 3} .3235776
{txt}{space 14}_Icowcode_790 {c |}{col 29}{res}{space 2} -.182564{col 41}{space 2} .2479614{col 52}{space 1}   -0.74{col 61}{space 3}0.462{col 69}{space 4}-.6686122{col 82}{space 3} .3034842
{txt}{space 14}_Icowcode_800 {c |}{col 29}{res}{space 2}-.1301017{col 41}{space 2} .2500947{col 52}{space 1}   -0.52{col 61}{space 3}0.603{col 69}{space 4}-.6203316{col 82}{space 3} .3601282
{txt}{space 14}_Icowcode_811 {c |}{col 29}{res}{space 2}-.2331676{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223454{col 82}{space 3} .2560101
{txt}{space 14}_Icowcode_812 {c |}{col 29}{res}{space 2}-.2331676{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223454{col 82}{space 3} .2560101
{txt}{space 14}_Icowcode_817 {c |}{col 29}{res}{space 2}-.1075906{col 41}{space 2} .2479746{col 52}{space 1}   -0.43{col 61}{space 3}0.664{col 69}{space 4}-.5936648{col 82}{space 3} .3784836
{txt}{space 14}_Icowcode_820 {c |}{col 29}{res}{space 2} .0352747{col 41}{space 2} .2758596{col 52}{space 1}    0.13{col 61}{space 3}0.898{col 69}{space 4} -.505459{col 82}{space 3} .5760083
{txt}{space 14}_Icowcode_830 {c |}{col 29}{res}{space 2}-.0373923{col 41}{space 2} .2463833{col 52}{space 1}   -0.15{col 61}{space 3}0.879{col 69}{space 4}-.5203471{col 82}{space 3} .4455626
{txt}{space 14}_Icowcode_835 {c |}{col 29}{res}{space 2}-.0883361{col 41}{space 2} .2740551{col 52}{space 1}   -0.32{col 61}{space 3}0.747{col 69}{space 4}-.6255325{col 82}{space 3} .4488604
{txt}{space 14}_Icowcode_840 {c |}{col 29}{res}{space 2} -.029317{col 41}{space 2} .2464217{col 52}{space 1}   -0.12{col 61}{space 3}0.905{col 69}{space 4}-.5123471{col 82}{space 3}  .453713
{txt}{space 14}_Icowcode_850 {c |}{col 29}{res}{space 2}-.0565359{col 41}{space 2} .2463526{col 52}{space 1}   -0.23{col 61}{space 3}0.818{col 69}{space 4}-.5394305{col 82}{space 3} .4263588
{txt}{space 14}_Icowcode_860 {c |}{col 29}{res}{space 2} .1411494{col 41}{space 2} .2498797{col 52}{space 1}    0.56{col 61}{space 3}0.572{col 69}{space 4}-.3486591{col 82}{space 3} .6309579
{txt}{space 14}_Icowcode_900 {c |}{col 29}{res}{space 2} -.049949{col 41}{space 2} .2463545{col 52}{space 1}   -0.20{col 61}{space 3}0.839{col 69}{space 4}-.5328474{col 82}{space 3} .4329495
{txt}{space 14}_Icowcode_910 {c |}{col 29}{res}{space 2} .1005208{col 41}{space 2} .2788593{col 52}{space 1}    0.36{col 61}{space 3}0.719{col 69}{space 4}-.4460928{col 82}{space 3} .6471345
{txt}{space 14}_Icowcode_920 {c |}{col 29}{res}{space 2}-.0602719{col 41}{space 2} .2463526{col 52}{space 1}   -0.24{col 61}{space 3}0.807{col 69}{space 4}-.5431666{col 82}{space 3} .4226228
{txt}{space 14}_Icowcode_935 {c |}{col 29}{res}{space 2}-.0897907{col 41}{space 2} .2464466{col 52}{space 1}   -0.36{col 61}{space 3}0.716{col 69}{space 4}-.5728697{col 82}{space 3} .3932883
{txt}{space 14}_Icowcode_940 {c |}{col 29}{res}{space 2}-.1572622{col 41}{space 2} .2473658{col 52}{space 1}   -0.64{col 61}{space 3}0.525{col 69}{space 4}-.6421429{col 82}{space 3} .3276185
{txt}{space 14}_Icowcode_946 {c |}{col 29}{res}{space 2}-.2331676{col 41}{space 2} .2495579{col 52}{space 1}   -0.93{col 61}{space 3}0.350{col 69}{space 4}-.7223454{col 82}{space 3} .2560101
{txt}{space 14}_Icowcode_947 {c |}{col 29}{res}{space 2}-.0644889{col 41}{space 2} .2463545{col 52}{space 1}   -0.26{col 61}{space 3}0.794{col 69}{space 4}-.5473873{col 82}{space 3} .4184096
{txt}{space 14}_Icowcode_950 {c |}{col 29}{res}{space 2}-.0602719{col 41}{space 2} .2463526{col 52}{space 1}   -0.24{col 61}{space 3}0.807{col 69}{space 4}-.5431666{col 82}{space 3} .4226228
{txt}{space 14}_Icowcode_955 {c |}{col 29}{res}{space 2}-.0644889{col 41}{space 2} .2463545{col 52}{space 1}   -0.26{col 61}{space 3}0.794{col 69}{space 4}-.5473873{col 82}{space 3} .4184096
{txt}{space 14}_Icowcode_970 {c |}{col 29}{res}{space 2}-.1530452{col 41}{space 2} .2472797{col 52}{space 1}   -0.62{col 61}{space 3}0.536{col 69}{space 4}-.6377573{col 82}{space 3} .3316669
{txt}{space 14}_Icowcode_983 {c |}{col 29}{res}{space 2}-.0223192{col 41}{space 2}  .246508{col 52}{space 1}   -0.09{col 61}{space 3}0.928{col 69}{space 4}-.5055185{col 82}{space 3} .4608801
{txt}{space 14}_Icowcode_986 {c |}{col 29}{res}{space 2}-.0560549{col 41}{space 2} .2463545{col 52}{space 1}   -0.23{col 61}{space 3}0.820{col 69}{space 4}-.5389534{col 82}{space 3} .4268435
{txt}{space 14}_Icowcode_987 {c |}{col 29}{res}{space 2}-.1359176{col 41}{space 2} .2500281{col 52}{space 1}   -0.54{col 61}{space 3}0.587{col 69}{space 4} -.626017{col 82}{space 3} .3541817
{txt}{space 14}_Icowcode_990 {c |}{col 29}{res}{space 2}-.1319604{col 41}{space 2} .2469066{col 52}{space 1}   -0.53{col 61}{space 3}0.593{col 69}{space 4}-.6159411{col 82}{space 3} .3520203
{txt}{space 13}_Icowcode_1013 {c |}{col 29}{res}{space 2} -.043404{col 41}{space 2} .2463833{col 52}{space 1}   -0.18{col 61}{space 3}0.860{col 69}{space 4}-.5263589{col 82}{space 3} .4395509
{txt}{space 13}_Icowcode_1015 {c |}{col 29}{res}{space 2}-.1368839{col 41}{space 2} .2500523{col 52}{space 1}   -0.55{col 61}{space 3}0.584{col 69}{space 4}-.6270308{col 82}{space 3} .3532629
{txt}{space 13}_Icowcode_1020 {c |}{col 29}{res}{space 2}-.1604193{col 41}{space 2} .2474556{col 52}{space 1}   -0.65{col 61}{space 3}0.517{col 69}{space 4} -.645476{col 82}{space 3} .3246375
{txt}{space 13}_Icowcode_1027 {c |}{col 29}{res}{space 2}        0{col 41}{txt}  (omitted)
{space 22}_cons {c |}{col 29}{res}{space 2}-.2641423{col 41}{space 2} .2814936{col 52}{space 1}   -0.94{col 61}{space 3}0.348{col 69}{space 4}-.8159196{col 82}{space 3}  .287635
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}({res}est4{txt} stored)

{com}. 
. esttab using "TableA6.tex", label keep(L7be_cat_1_China_carryover L7be_cat_2_China_carryover L7be_cat_3_China_carryover  L14be_cat_1_China_carryover L14be_cat_2_China_carryover L14be_cat_3_China_carryover) order(L7be_cat_1_China_carryover L7be_cat_2_China_carryover L7be_cat_3_China_carryover  L14be_cat_1_China_carryover L14be_cat_2_China_carryover L14be_cat_3_China_carryover ) indicate("Country FE = *cow*" "Time trend = *time*") nonotes se(3) b(3) replace star(+ 0.10 * 0.05 ** 0.01) compress nogaps nodepvars mtitles("" "" "" "" "" "" "" "")  
{res}{txt}(note: file TableA6.tex not found)
(output written to {browse  `"TableA6.tex"'})

{com}. 
. * Table A5: Entry Restrictions by Category and Monthly Passenger Volume (Jan-Mar)
. 
. duplicates drop cowcode month, force

{p 0 4}{txt}Duplicates in terms of {res} cowcode month{p_end}

{txt}(13,650 observations deleted)

{com}. 
. eststo clear 
{txt}
{com}. eststo: xtreg log_passenger_china be_cat_1_China_carryover be_cat_2_China_carryover be_cat_3_China_carryover, fe 
{res}
{txt}Fixed-effects (within) regression{col 49}Number of obs{col 67}={col 69}{res}       469
{txt}Group variable: {res}cowcode{txt}{col 49}Number of groups{col 67}={col 69}{res}       174

{txt}R-sq:{col 49}Obs per group:
     within  = {res}0.1530{col 63}{txt}min{col 67}={col 69}{res}         1
{txt}     between = {res}0.0421{col 63}{txt}avg{col 67}={col 69}{res}       2.7
{txt}     overall = {res}0.0000{col 63}{txt}max{col 67}={col 69}{res}         3

{txt}{col 49}F({res}3{txt},{res}292{txt}){col 67}={col 70}{res}    17.59
{txt}corr(u_i, Xb){col 16}= {res}-0.1680{txt}{col 49}Prob > F{col 67}={col 73}{res}0.0000

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     log_passenger_china{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
be_cat_1_China_carryover {c |}{col 26}{res}{space 2}-1.621316{col 38}{space 2} .2732641{col 49}{space 1}   -5.93{col 58}{space 3}0.000{col 66}{space 4}-2.159133{col 79}{space 3}-1.083499
{txt}be_cat_2_China_carryover {c |}{col 26}{res}{space 2}-1.403638{col 38}{space 2} .7933959{col 49}{space 1}   -1.77{col 58}{space 3}0.078{col 66}{space 4}-2.965138{col 79}{space 3}  .157861
{txt}be_cat_3_China_carryover {c |}{col 26}{res}{space 2}-.9734596{col 38}{space 2} .2792879{col 49}{space 1}   -3.49{col 58}{space 3}0.001{col 66}{space 4}-1.523132{col 79}{space 3}-.4237871
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 5.844681{col 38}{space 2} .0691037{col 49}{space 1}   84.58{col 58}{space 3}0.000{col 66}{space 4} 5.708676{col 79}{space 3} 5.980685
{txt}{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                 sigma_u {c |} {res} 3.4760452
                 {txt}sigma_e {c |} {res} 1.2944087
                     {txt}rho {c |} {res} .87822015{txt}   (fraction of variance due to u_i)
{hline 25}{c BT}{hline 64}
F test that all u_i=0: F({res}173{txt}, {res}292{txt}) = {res}17.50{col 62}{txt}Prob > F = {res}0.0000
{txt}({res}est1{txt} stored)

{com}. eststo: xi: xtreg log_passenger_china be_cat_1_China_carryover be_cat_2_China_carryover be_cat_3_China_carryover time time_sq time_cube, fe 
{p 0 6 2}{txt}note: time_cube omitted because of collinearity{p_end}
{res}
{txt}Fixed-effects (within) regression{col 49}Number of obs{col 67}={col 69}{res}       469
{txt}Group variable: {res}cowcode{txt}{col 49}Number of groups{col 67}={col 69}{res}       174

{txt}R-sq:{col 49}Obs per group:
     within  = {res}0.4236{col 63}{txt}min{col 67}={col 69}{res}         1
{txt}     between = {res}0.1137{col 63}{txt}avg{col 67}={col 69}{res}       2.7
{txt}     overall = {res}0.0138{col 63}{txt}max{col 67}={col 69}{res}         3

{txt}{col 49}F({res}5{txt},{res}290{txt}){col 67}={col 70}{res}    42.62
{txt}corr(u_i, Xb){col 16}= {res}-0.1010{txt}{col 49}Prob > F{col 67}={col 73}{res}0.0000

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     log_passenger_china{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
be_cat_1_China_carryover {c |}{col 26}{res}{space 2}-.2962307{col 38}{space 2}  .254768{col 49}{space 1}   -1.16{col 58}{space 3}0.246{col 66}{space 4}-.7976594{col 79}{space 3} .2051981
{txt}be_cat_2_China_carryover {c |}{col 26}{res}{space 2}-.2073167{col 38}{space 2} .6649519{col 49}{space 1}   -0.31{col 58}{space 3}0.755{col 66}{space 4} -1.51606{col 79}{space 3} 1.101427
{txt}be_cat_3_China_carryover {c |}{col 26}{res}{space 2} .3919242{col 38}{space 2} .2664675{col 49}{space 1}    1.47{col 58}{space 3}0.142{col 66}{space 4}-.1325313{col 79}{space 3} .9163798
{txt}{space 20}time {c |}{col 26}{res}{space 2}-.0556052{col 38}{space 2} .0111767{col 49}{space 1}   -4.98{col 58}{space 3}0.000{col 66}{space 4}-.0776029{col 79}{space 3}-.0336075
{txt}{space 17}time_sq {c |}{col 26}{res}{space 2} .0003238{col 38}{space 2} .0002214{col 49}{space 1}    1.46{col 58}{space 3}0.145{col 66}{space 4}-.0001119{col 79}{space 3} .0007595
{txt}{space 15}time_cube {c |}{col 26}{res}{space 2}        0{col 38}{txt}  (omitted)
{space 19}_cons {c |}{col 26}{res}{space 2} 6.503563{col 38}{space 2} .0924274{col 49}{space 1}   70.36{col 58}{space 3}0.000{col 66}{space 4} 6.321649{col 79}{space 3} 6.685476
{txt}{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                 sigma_u {c |} {res} 3.4915517
                 {txt}sigma_e {c |} {res} 1.0715434
                     {txt}rho {c |} {res} .91392208{txt}   (fraction of variance due to u_i)
{hline 25}{c BT}{hline 64}
F test that all u_i=0: F({res}173{txt}, {res}290{txt}) = {res}25.79{col 62}{txt}Prob > F = {res}0.0000
{txt}({res}est2{txt} stored)

{com}. eststo: xi: xtreg log_passenger_china be_cat_1_China_carryover be_cat_2_China_carryover be_cat_3_China_carryover i.time, fe 
{txt}i.time{col 19}_Itime_1-47{col 39}(naturally coded; _Itime_1 omitted)
{res}
{txt}Fixed-effects (within) regression{col 49}Number of obs{col 67}={col 69}{res}       469
{txt}Group variable: {res}cowcode{txt}{col 49}Number of groups{col 67}={col 69}{res}       174

{txt}R-sq:{col 49}Obs per group:
     within  = {res}0.4236{col 63}{txt}min{col 67}={col 69}{res}         1
{txt}     between = {res}0.1137{col 63}{txt}avg{col 67}={col 69}{res}       2.7
{txt}     overall = {res}0.0138{col 63}{txt}max{col 67}={col 69}{res}         3

{txt}{col 49}F({res}5{txt},{res}290{txt}){col 67}={col 70}{res}    42.62
{txt}corr(u_i, Xb){col 16}= {res}-0.1010{txt}{col 49}Prob > F{col 67}={col 73}{res}0.0000

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     log_passenger_china{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      t{col 58}   P>|t|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
be_cat_1_China_carryover {c |}{col 26}{res}{space 2}-.2962307{col 38}{space 2}  .254768{col 49}{space 1}   -1.16{col 58}{space 3}0.246{col 66}{space 4}-.7976594{col 79}{space 3} .2051981
{txt}be_cat_2_China_carryover {c |}{col 26}{res}{space 2}-.2073167{col 38}{space 2} .6649519{col 49}{space 1}   -0.31{col 58}{space 3}0.755{col 66}{space 4} -1.51606{col 79}{space 3} 1.101427
{txt}be_cat_3_China_carryover {c |}{col 26}{res}{space 2} .3919242{col 38}{space 2} .2664675{col 49}{space 1}    1.47{col 58}{space 3}0.142{col 66}{space 4}-.1325313{col 79}{space 3} .9163798
{txt}{space 15}_Itime_18 {c |}{col 26}{res}{space 2}-.8406958{col 38}{space 2} .1240054{col 49}{space 1}   -6.78{col 58}{space 3}0.000{col 66}{space 4}-1.084761{col 79}{space 3} -.596631
{txt}{space 15}_Itime_47 {c |}{col 26}{res}{space 2}-1.842853{col 38}{space 2} .1592202{col 49}{space 1}  -11.57{col 58}{space 3}0.000{col 66}{space 4}-2.156227{col 79}{space 3} -1.52948
{txt}{space 19}_cons {c |}{col 26}{res}{space 2} 6.448281{col 38}{space 2} .0850037{col 49}{space 1}   75.86{col 58}{space 3}0.000{col 66}{space 4} 6.280979{col 79}{space 3} 6.615584
{txt}{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                 sigma_u {c |} {res} 3.4915517
                 {txt}sigma_e {c |} {res} 1.0715434
                     {txt}rho {c |} {res} .91392208{txt}   (fraction of variance due to u_i)
{hline 25}{c BT}{hline 64}
F test that all u_i=0: F({res}173{txt}, {res}290{txt}) = {res}25.79{col 62}{txt}Prob > F = {res}0.0000
{txt}({res}est3{txt} stored)

{com}. 
. esttab using "TableA5.tex", label keep(be_cat_1_China_carryover be_cat_2_China_carryover be_cat_3_China_carryover be_cat_2_China_carryover be_cat_3_China_carryover ) order(be_cat_1_China_carryover  be_cat_2_China_carryover be_cat_3_China_carryover be_cat_3_China_carryover ) nonotes se(3) b(3) replace star(+ 0.10 * 0.05 ** 0.01) compress nogaps nodepvars mtitles("" "" "") 
{res}{txt}(note: file TableA5.tex not found)
(output written to {browse  `"TableA5.tex"'})

{com}. 
{txt}end of do-file

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\user\Box\COVID-19 IPE\Analysis\FPA\Dataverse\fpa_replication_log.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}19 Jul 2022, 11:20:06
{txt}{.-}
{smcl}
{txt}{sf}{ul off}